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FEEDING YOUR QUALITATIVE NEEDS

Have you ever searched "free qualitative research software" only to be disappointed that nothing lets you tag your materials? Search no more! Taguette is a free and open-source tool for qualitative research. You can import your research materials, highlight and tag quotes, and export the results!

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Taguette works both on your local computer (macOS, Windows, Linux) and on a server. When running Taguette locally, your data is as secure as your computer . That means that if you can have your data on your computer, you can run Taguette with no worries.

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Highlight & Tag

Taguette allows you to upload your research materials and tag them, just as you would use different color highlighters with printed paper. You can add new tags, then just select some text, click 'new highlight', and add whatever tag you find to be most relevant!

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Export Results

After you've done your work highlighting materials in Taguette, you can export in a variety of ways -- your whole project, codebook, all your highlighted quotes (or ones for a specific tag!), and highlighted documents. It's a good practice to keep an archival copy of your work!

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The world’s most powerful AI-based qualitative data analysis solution.

QualAI utilizes advanced AI technology to increase researcher efficiency, enhance data reliability, and mitigate bias.

researchers

QualAI aids researchers with data codification, thematic analyses, and content summaries to increase data reliability and mitigate bias.

organizations

QualAI helps organizations with market research, consumer analysis, business development, data aggregation and interpretation.

See how QualAI helps students analyze large-scale qualitative data sets, codify transcripts, and generate themes to reduce bias and increase efficiency.

ERIK ALANSON, Ph.d.

Co-Founder, QualAI

Academic Researcher

University Professor

tonkia bridges, ed.d.

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Master Your Research Projects with the Power of AI

ATLAS.ti bridges human expertise with AI efficiency to provide fast and accurate insights. Communicate directly with your documents and have them automatically coded based on your intentions, providing customized results. Only ATLAS.ti offers complete insight and verifiability of your AI analysis results at all times. This enables you to unpack the black box of AI in your research and uniquely make it your own.

Import and organize your files

Import or upload data from any source and manage everything in one software package. Then, start discovering valuable insights using intelligent data analysis tools to make more informed decisions – in hours, not days. Easily import:

• Audio Files
• Social Media Data
• Survey Files
• Reference Manager Data
• Other Qualitative Data

Analyze and refine your data

Anyone can take control of their qualitative analysis without prior knowledge: edit content, refine, and structure your data the way you need. Simply use codes to tag qualitative insights, and embrace AI Coding plus team collaboration for lightning-speed outcomes. Unlock insights for:

• Survey Data Analysis
• Interview Analysis
• Focus Group Analysis
• Literature Review and Analysis
• Content Analysis
• UX Research
• Bachelor & Master Thesis
• Doctoral Dissertations
• Term Papers
• Customer Feedback
• Qualitative Customer Data

Visualize and share your insights

Let your data speak and get a deep understanding of the meaning behind your research findings. With ATLAS.ti, you can share your insights through clear visualizations that turn dull numbers into eye-opening results – presenting your conclusions with confidence. Make use of:

• Sankey Charts
• Donut Charts
• Word Clouds

Why our users love ATLAS.ti

Get all-in-one platform access to our desktop apps on Windows and Mac, plus our Web version for browsers. Leverage our cutting-edge QDA software today!

One Package, All Features

ATLAS.ti licenses offer access to all features on all platforms for one flat fee. No hidden costs, no surprises. Moreover, our multi-license options deliver exceptional flexibility for teams, simplifying the qualitative research process.

Free Live Support, 24/5

Our qualitative data experts and ATLAS.ti specialists work around the clock to provide the support you need. We will do everything to help you keep your analysis project running and uncompromised.

Full Compatibility: Win, Mac, Web

With ATLAS.ti, you can choose whether you want to work with our desktop apps on Windows and Mac or our Web version at no extra cost. Seamlessly exchange projects with each other – no matter which platform you use.

Team Collaboration in Real-Time

Working together with your entire team doesn’t have to be complicated. ATLAS.ti Web has simplified the process for you – easily invite team members and collaborate efficiently on your projects in real-time.

OpenAI-powered Tools

Reduce your overall data analysis time by more than 90% with the power of OpenAI and uncover insights that otherwise may have been missed. Enabled by leading AI technology, we provide a game-changing solution that makes automatic data coding a reality.

License Management Redefined

"My ATLAS.ti" enables you to manage users and licenses the way you want for the best efficiency: Share multi-user licenses with as many people as you want, with no restrictions on people, seats, or machines. When one user logs off, the seat instantly opens up for another user.

If you're considering ATLAS.ti for qualitative data analysis, you're making a wise choice. Our top-rated QDA software is perfect for students, researchers, academic institutions, and commercial enterprises – offering a wide array of AI-driven analysis tools to help you succeed. Here are just a few reasons to choose ATLAS.ti:

An intuitive interface made for qualitative research

ATLAS.ti caters to both research professionals and beginners. It covers everything from qualitative text analysis and evaluation of customer interviews to web content analysis and specific business intelligence tasks.

Users can collect and analyze data on the major operating systems (Windows and macOS), even with our Web version for browsers. Our user-friendly qualitative data analysis software makes it easy to upload files and analyze data quickly and efficiently so that you can make the most out of your research.

Quick and easy AI-powered coding

Transforming text-based data into valuable insights can be time-consuming. With ATLAS.ti, you can import data from any source and gain deeper insights using AI. In addition, our qualitative software offers tools to automatically create coded segments in your data and quickly identify themes.

ATLAS.ti's qualitative data analysis tools enable you to organize all your text data (i.e., from customer interviews or focus groups) in one place. This way, you can analyze qualitative data faster than ever. Plus, you can utilize a code hierarchy with a tree structure for better code management.

Our automatic AI Coding feature uses OpenAI's GPT model, which can understand natural language on a human-like level. More than text mining: This groundbreaking analysis tool empowers users from all fields of work by drastically reducing the overall coding and analysis time.

Cover all your qualitative data analysis needs

Whether you rely on transcripts from focus groups, observation notes, survey responses, or even audio and video files – you can analyze it all with ATLAS.ti. Unlike quantitative data tools, our software supports all major forms of data so that you can conduct qualitative data analysis on any research project, even customer feedback, textual data, pictures, and video recordings.

Whatever it is, you can import data into one central location in ATLAS.ti – enabling you to leverage qualitative and mixed methods for your research projects.

Powerful data analysis on autopilot

Our artificial intelligence and machine learning tools make finding insights in your research project easy. Qualitative data analysis tools such as Sentiment Analysis and Opinion Mining can perform text analysis across multiple documents to analyze large projects faster and more insightful.

Whether you want to analyze customer data or identify keywords from research materials, our AI tools can help you finish the job quickly. Regardless of what you want to achieve: pure qualitative analysis or mixed methods research, ATLAS.ti offers the leading solution trusted by academics and businesses.

Deeper insights through qualitative analysis

We understand that any qualitative data analysis tool is only as powerful as the insights they provide to you and your audience. ATLAS.ti is more than a text analyzer – we develop our software to visualize your data analysis in multiple formats:

Bar charts, Sankey diagrams, word clouds, and network visualizations help you identify data themes and patterns for robust and accurate insights.

Seamless collaboration across teams

Qualitative and mixed methods research often relies on collaboration between team members. That's why multi-user licenses for ATLAS.ti allow you to share our qualitative data analysis software with your colleagues.

Now all your team members can work together on the same project to analyze qualitative data. Unlike other software, ATLAS.ti allows you to capture customer insights with the collective power of your colleagues.

Expert support and training

Customer satisfaction is our top priority at ATLAS.ti. We offer technical and methodological support for all users, whether conducting mixed methods research, qualitative research, statistical analysis, thematic analysis, market research, or academic research.

ATLAS.ti experts worldwide are always just a click away from supporting the users of our #1 software for qualitative data analysis.

webQDA – Qualitative Data Analysis Software

webQDA is a qualitative, web-based data analysis software intended for all researchers and professionals conducting qualitative research. webQDA allows you to analyze text, image, video, audio, tables, PDF files, Youtube videos, etc. in a collaborative, synchronous or asynchronous manner.

and intuitive

Collaborative

environment

Price adjusted

to your needs

Easy Importing of Projects

With webQDA Software you can import your projects quickly and easily. Get to work right away.

Intuitive software

The Platform makes your user experience unique, making your day-to-day work easier.

100% online

Work on your Projects on any device with internet access.

100% compatible

The Platform is compatible with all existing operating systems.

Total data security

Work in a safe environment!

Seven Essential Steps (Cross-cutting Subtasks) for Qualitative Data Analysis with Integration of webQDA Software

Automatic Coding in webQDA (1st version)

Automatic Coding in webQDA (1st version) One of the main objectives of using tools to support qualitative data analysis, such as webQDA, […]

Ethics in Qualitative Research: a reflection…

Ethics in Qualitative Research: a reflection… António Moreira Research Center on Didactics and Technology in the Education of Trainers (CIDTFF), Department of Education […]

Six ways to represent results graphically with webQDA

Six ways to represent results graphically with webQDA By: Sonia Verdugo (University of Salamanca) The visual representation of results offers the researcher […]

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10 best qualitative data analysis tools

A lot of teams spend a lot of time collecting qualitative customer experience data—but how do you make sense of it, and how do you turn insights into action?

Qualitative data analysis tools help you make sense of customer feedback so you can focus on improving the user and product experience and creating customer delight.

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This chapter of Hotjar's qualitative data analysis (QDA) guide covers the ten best QDA tools that will help you make sense of your customer insights and better understand your users.

Collect qualitative customer data with Hotjar

Use Hotjar’s Surveys and Feedback widget to collect user insights and better understand your customers.

10 tools for qualitative data analysis 

Qualitative data analysis involves gathering, structuring, and interpreting contextual data to identify key patterns and themes in text, audio, and video.

Qualitative data analysis software automates this process, allowing you to focus on interpreting the results—and make informed decisions about how to improve your product—rather than wading through pages of often subjective, text-based data.

Pro tip: before you can analyze qualitative data, you need to gather it. 

One way to collect qualitative customer insights is to place Hotjar Surveys on key pages of your site . Surveys make it easy to capture voice-of-the-customer (VoC) feedback about product features, updated designs, and customer satisfaction—or to perform user and market research.

Need some ideas for your next qualitative research survey? Check out our Hotjar Survey Templates for inspiration.

Example product discovery questions from Hotjar’s bank of survey templates

1. Cauliflower

Cauliflower is a no-code qualitative data analysis tool that gives researchers, product marketers, and developers access to AI-based analytics without dealing with complex interfaces.

How Cauliflower analyzes qualitative data

Cauliflower’s AI-powered analytics help you understand the differences and similarities between different pieces of customer feedback. Ready-made visualizations help identify themes in customers’ words without reading through every review, and make it easy to:

Analyze customer survey data and answers to open-ended questions

Process and understand customer reviews

Examine your social media channels

Identify and prioritize product testing initiatives

Visualize results and share them with your team

One of Cauliflower’s customers says, “[Cauliflower is] great for visualizing the output, particularly finding relevant patterns in comparing breakouts and focussing our qualitative analysis on the big themes emerging.”

NVivo is one of the most popular qualitative data analysis tools on the market—and probably the most expensive. It’s a more technical solution than Cauliflower, and requires more training. NVivo is best for tech-savvy customer experience and product development teams at mid-sized companies and enterprises.

How NVivo analyzes qualitative data

NVivo’s Transcription tool transcribes and analyzes audio and video files from recorded calls—like sales calls, customer interviews, and product demos—and lets you automatically transfer text files into NVivo for further analysis to:

Find recurring themes in customer feedback

Analyze different types of qualitative data, like text, audio, and video

Code and visualize customer input

Identify market gaps based on qualitative and consumer-focused research

Dylan Hazlett from Adial Pharmaceuticals says, “ We needed a reliable software to perform qualitative text analysis. The complexity and features of [Nvivo] have created great value for our team.”

3. ​​Quirkos

Quirkos is a simple and affordable qualitative data analysis tool. Its text analyzer identifies common keywords within text documents to help businesses quickly and easily interpret customer reviews and interviews.

How Quirkos analyzes qualitative data

Quirkos displays side-by-side comparison views to help you understand the difference between feedback shared by different audience groups (by age group, location, gender, etc.). You can also use it to:

Identify keywords and phrases in survey responses and customer interviews

Visualize customer insights

Collaborate on projects

Color code texts effortlessly

One of Quirkos's users says, “ The interface is intuitive, easy to use, and follows quite an intuitive method of assigning codes to documents.”

4. Qualtrics

Qualtrics is a sophisticated experience management platform. The platform offers a range of tools, but we’ll focus on Qualtrics CoreXM here.  

Qualtrics CoreXM lets you collect and analyze insights to remove uncertainty from product development. It helps validate product ideas, spot gaps in the market, and identify broken product experiences, and the tool uses predictive intelligence and analytics to put your customer opinion at the heart of your decision-making.

How Qualtrics analyzes qualitative data

Qualtrics helps teams streamline multiple processes in one interface. You can gather and analyze qualitative data, then immediately share results and hypotheses with stakeholders. The platform also allows you to:

Collect customer feedback through various channels

Understand emotions and sentiment behind customers’ words

Predict what your customers will do next

Act immediately based on the results provided through various integrations

A user in project management shares, “The most useful part of Qualtrics is the depth of analytics you receive on your surveys, questionnaires, and other tools. In real-time, as you develop your surveys, you are given insights into how your data can be analyzed. It is designed to help you get the data you need without asking unnecessary questions.”

5. Dovetail

Dovetail is a customer research platform for growing businesses. It offers three core tools: Playback, Markup, and Backstage. For qualitative data analysis, you’ll need Markup.

Markup offers tools for transcription and analysis of all kinds of qualitative data, and is a great way to consolidate insights.

How Dovetail analyzes qualitative data

Dovetail’s charts help you easily quantify qualitative data. If you need to present your findings to the team, the platform makes it easy to loop in your teammates, manage access rights, and collaborate through the interface. You can:

Transcribe recordings automatically

Discover meaningful patterns in textual data

Highlight and tag customer interviews

Run sentiment analysis

Collaborate on customer research through one interface

Kathryn Rounding , Senior Product Designer at You Need A Budget, says, “Dovetail is a fantastic tool for conducting and managing qualitative research. It helps bring all your research planning, source data, analysis, and reporting together, so you can not only share the final results but all the supporting work that helped you get there.”

6. Thematic

Thematic's AI-driven text feedback analysis platform helps you understand what your customers are saying—and why they’re saying it.

How Thematic analyzes qualitative data

Thematic helps you connect feedback from different channels, uncover themes in customer experience data, and run sentiment analysis—all to make better product decisions. Thematic is helpful when you need to:

Analyze unstructured feedback data from across channels

Discover relationships and patterns in feedback

Reveal emerging trends in customer feedback

Split insights by customer segment

Use resulting data in predictive analytics

Emma Glazer , Director of Marketing at DoorDash, says, “Thematic empowers us with information to help make the right decisions, and I love seeing themes as they emerge. We get real-time signals on issues our customers are experiencing and early feedback on new features they love. I love looking at the week-over-week breakdowns and comparing segments of our audience (market, tenure, etc.) Thematic helps me understand what’s driving our metrics and what steps we need to take next.” 

Delve is cloud-based qualitative data analysis software perfect for coding large volumes of textual data, and is best for analyzing long-form customer interviews.

How Delve analyzes qualitative data

Delve helps reveal the core themes and narratives behind transcripts from sales calls and customer interviews. It also helps to:

Find, group, and refine themes in customer feedback

Analyze long-form customer interviews

Categorize your data by code, pattern, and demographic information

Perform thematic analysis, narrative analysis, and grounded theory analysis

One Delve user says, “Using Delve, it is easier to focus just on coding to start, without getting sidetracked analyzing what I am reading. Once coding is finished, the selected excerpts are already organized based on my own custom outline and I can begin analyzing right away, rather than spending time organizing my notes before I can begin the analysis and writing process.”

8. ATLAS.ti

ATLAS.ti is a qualitative data analysis tool that brings together customer and product research data. It has a range of helpful features for marketers, product analysts, UX professionals, and product designers.

How ATLAS.ti analyzes qualitative data

ATLAS.ti helps product teams collect, structure, and evaluate user feedback before realizing new product ideas. To enhance your product design process with ATLAS.ti, you can:

Generate qualitative insights from surveys

Apply any method of qualitative research

Analyze open-ended questions and standardized surveys

Perform prototype testing

Visualize research results with charts

Collaborate with your team through a single platform

One of the ATLAS.ti customers shares,“ATLAS.ti is innovating in the handling of qualitative data. It gives the user total freedom and the possibility of connecting with other software, as it has many export options.” 

MAXQDA is a data analysis software that can analyze and organize a wide range of data, from handwritten texts, to video recordings, to Tweets.

How MAXQDA analyzes qualitative data

MAWQDA organizes your customer interviews and turns the data into digestible statistics by enabling you to:

Easily transcribe audio or video interviews

Structure standardized and open-ended survey responses

Categorize survey data

Combine qualitative and quantitative methods to get deeper insights into customer data

Share your work with team members

One enterprise-level customer says MAXQDA has “lots of useful features for analyzing and reporting interview and survey data. I really appreciated how easy it was to integrate SPSS data and conduct mixed-method research. The reporting features are high-quality and I loved using Word Clouds for quick and easy data representation.”

10. MonkeyLearn

MonkeyLearn is no-code analytics software for CX and product teams.

How MonkeyLearn analyzes qualitative data

MonkeyLearn automatically sorts, visualizes, and prioritizes customer feedback with its AI-powered algorithms. Along with organizing your data into themes, the tool will split it by intent—allowing you to promptly distinguish positive reviews from issues and requests and address them immediately.

One MonkeyLearn user says, “I like that MonkeyLearn helps us pull data from our tickets automatically and allows us to engage with our customers properly. As our tickets come in, the AI classifies data through keywords and high-end text analysis. It highlights specific text and categorizes it for easy sorting and processing.”

The next step in automating qualitative data analysis 

Qualitative data analysis tools help you uncover actionable insights from customer feedback, reviews, interviews, and survey responses—without getting lost in data.

But there's no one tool to rule them all: each solution has specific functionality, and your team might need to use the tools together depending on your objectives.

With the right qualitative data analysis software, you can make sense of what your customers really want and create better products for them, achieving customer delight and loyalty.

FAQs about qualitative data analysis software

What is qualitative data analysis software.

Qualitative data analysis software is technology that compiles and organizes contextual, non-quantifiable data, making it easy to interpret qualitative customer insights and information.

Which software is used for qualitative data analysis?

The best software used for qualitative data analysis is:

Cauliflower

MonkeyLearn

Is NVivo the only tool for qualitative data analysis?

NVivo isn’t the only tool for qualitative data analysis, but it’s one of the best (and most popular) software providers for qualitative and mixed-methods research.

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Qualitative data brought to life. Qualitative data brought to life.

Quirkos is simple qualitative analysis software, designed to immerse you in your qualitative text data and help you to understand it quickly and easily.

Helping qualitative researchers understand their data for 9+ years

Quirkos is the best tool for qualitative research - it really helps with visualising the data and identifying patterns within multiple datasets!

Oliver, PhD Candidate

Work together with Quirkos Cloud

Code qualitative text with an unlimited number of simultaneous users with no extra costs. Work from any device and see edits from colleagues pop up in real time.

With live chat and all your collaborative projects synced and backed up on our secure cloud servers, it's never been easier to stay on the same page.

Quick to learn, flexible to use

Quirkos is easy to master, with just the essential tools you need to help you focus on the richness of your small qualitative data sets. Organise your sources, canvas and codes in any way, and finely categorise and filter your data. Our flexible design is agnostic, so you can use grounded theory, thematic analysis, or IPA as you prefer.

Cloud collaboration and secure storage

With a Quirkos Cloud subscription, you can collaborate seamlessly with colleagues in real-time. Our automated transcription service makes it easy to bring in your recorded data and get analysing quickly. You'll also get unlimited cloud storage and the ability to work across all your devices, with your project data stored and backed up automatically on our secure servers.

Platform freedom

Enjoy the same intuitive experience and features across Windows, Mac, Linux, and any web browser - including tablets and smartphones with Quirkos Cloud.

Easy import

Cross-compatibility, data visualisation, side-by-side comparisons, robust export options, secure transcription, security by design, first-class support, make qualitative data analysis easier with quirkos.

Watch a video introduction to Quirkos, or view our free resources and tutorials for more in-depth guides.

Affordable qualitative software for everyone

I thoroughly enjoyed using your software to analyze my data. I've enjoyed it so much that I sing your praises at my university.

Kevin, PhD candidate

Our mantra is 'accessibility'; Quirkos is often half the price of alternative qualitative software, with great discounts for students, academics, non-profit organisations, and the public sector. For those in the global majority world, we also have a 25% discount to make qualitative analysis software accessible to everyone. Find out more on our pricing page .

Cloud or Offline?

We offer two options for purchasing Quirkos: a recurring annual or quarterly subscription, or a permanent, single-purchase software licence. Both provide full access to Quirkos Desktop , our desktop app for Windows, Mac and Linux, while Quirkos Cloud subscriptions allow you to access your synced data anywhere, including in the Web App with nothing to install.

As well as the below prices for individuals, we offer significantly discounted group rates for organisations and teams interested in using Quirkos.

Prices below are based on 1 year subscriptions; minimum subscription is 3 months.

Select a rate:

Quirkos Cloud

• Access to Quirkos Desktop on all devices you own for the duration of your subscription
• Unlimited, secure Cloud storage
• Access to Quirkos Web, our browser-based version which requires no download
• Live collaboration and project sharing

Offline Licence

$69 / lifetime.

• Lifetime access to Quirkos Desktop on one or two computers
• Store projects offline on your hard drive
• No registration or log-in required
• Collaborate with other Quirkos users offline, sending projects back and forth

Our next event

Ready to give Quirkos a try? Register for a free 14-day trial of Quirkos today, with no restrictions on features or projects. with no restrictions on features or projects. Want to learn more? Read more about our features or see Quirkos in action !

The Best 10 Qualitative Data Analysis Software Platforms in 2023

Published: June 08, 2023

Qualitative data analysis software has become essential in helping businesses know their customers. In fact, 63% of customers expect you to know their wants and expectations.

Qualitative data analysis software (QDAS) can help you review trends and consumer behavior. You'll then know what your target audience wants and give them great experiences.

In this post, we'll break down some of the best qualitative data analysis software you can use with your team. But first, let's define qualitative data analysis software.

What is qualitative analysis software?

Qualitative data analysis software (QDAS) gathers information beyond pure numbers to help you make better decisions.

These tools scrape your digital presence, chat messages, reviews, and files for customer insights. They then review the details in bulk, saving your team valuable time during reporting.

Additionally, QDAS minimizes data loss, as data is either stored in the cloud or on your computer. These tools account for errors and bias during analysis — common challenges when working with data manually.

With QDAS, your business can test and refine theories to predict future events or customer actions. With great data accuracy, you can reduce risks and achieve greater results.

Survey Tools vs. Qualitative Data Analysis Software

Traditional survey tools just provide you with reporting for quantitative data, such as age and number of visits per month. This narrow view of data limits your analysis to questions with quantifiable answers.

Alternatively, qualitative data analysis software (QDAS) gathers insights beyond numbers. That includes insights from interviews, focus groups, and online reviews. You can then get a broader view of customer concerns.

The 10 Best Qualitative Data Analysis Software

• Raven's Eye
• Square Feedback
• QDA Miner Lite

HubSpot offers a customer feedback tool that generates detailed analytics from surveys and customer reviews. The tool can analyze these responses and provides a detailed breakdown of customer satisfaction.

You can access data from one dashboard. This lets you view different charts and graphs summarizing your customers' responses. With a simple setup, your team has a quick, clean way to review customer insights.

HubSpot's customer feedback tool is part of HubSpot's Service Hub tools. It can collect both quantitative and qualitative customer feedback.

What We Love

• The tool connects with HubSpot's NPS surveys, so you can seamlessly collect and analyze customer data.
• There's an easy-to-use interface.
• The interface gathers customer service KPIs so you can discover growth opportunities.

MAXQDA is a qualitative data analysis software designed for companies analyzing a range of customer data.

The software allows you to import data from interviews, focus groups, surveys, videos, and social media. This way, all your qualitative data can be reviewed in one central location.

Once imported into MAXQDA, you can organize your information into different categories. You can mark specific data with tags and leave notes for other employees to review your work.

MAXQDA even lets you color code data so your team knows exactly what to work on each day.

• You can use MAXQDA with multiple data formats, including social media posts, videos, and images.
• MAXQDA supports both mixed method analysis and statistical analysis.
• You can store project data in one project pack to make collaboration easy.

Quirkos includes a variety of tools that analyze and review qualitative data. That includes comparative analysis, which shows side-by-side views of your data.

Your team can more easily spot trends and identify roadblocks in the customer experience.

With Quirkos, you'll also have unparalleled customization options. Quirkos, unlike other QDAS, has 16 million colors that you can use in your theme, making coding quicker.

This is useful if you have a large coding framework, as you can label your themes with different colors for easy identification and reading.

• Quirkos is compatible with many operating systems, including Linux and Mac.
• There's a drag-and-drop feature for coding sections of text.
• You can connect to SPSS, Word, or Excel to generate custom reports.

4. Qualtrics

Qualtrics comes with two key tools for simplifying your qualitative research process: TextIQ and DriverIQ.

TextIQ uses AI to analyze open-structured data. You can then assess customer sentiment and draw helpful insights from the data.

The DriverIQ tool helps you see what matters most for your customers, from purchase intent to satisfaction. You can then focus on what will have the most impact on your business.

• The TextIQ tools help uncover trends, problems, and opportunities from customer survey responses. Additionally, it acts as a social listening tool, so you can use it to identify brand mentions on social media.
• The advanced drag-and-drop feature allows you to visualize data quickly.
• Sophisticated intelligence tools (AI+ML) make advanced research for different metrics easier.

5. Raven's Eye

Raven's Eye is a qualitative data analysis software that can process and analyze natural language data. One of its most popular features is its audio converter, which uploads audio files into the software and transforms them into text files.

Then, it analyzes the text for unique insights into customer behavior. Raven's Eye is perfect for audio interviews with customers. You can upload the recorded session to Raven's Eye for analysis.

In addition to audio, Raven's Eye processes text documents. The text analyzer can review text samples written in over 65 different languages.

It then uses a "natural language" analysis to determine a variety of unique metrics, ranging from word count to reading ease.

• Raven's Eye quickly, accurately, and reliably convert audio and text files. This will help you understand how customers think and communicate.
• The program is cloud-based, meaning it can be accessed from multiple devices.
• You can explore text and spoken word (or natural language data) in the same way human beings can.

6. Square Feedback

Square Feedback is a free customer feedback collection tool that provides qualitative data reporting. It can analyze survey responses to see how satisfied your customers are.

Square Feedback also comes with historical filter options. With this feature, you can compare past data to current customer information.

• Square Feedback integrates into your digital receipts and easily collects feedback.
• You can privately track customer comments and responses.
• Square Feedback provides rich customer insights that you can use to make informed decisions.

LiGRE can be used by students, business professionals, and researchers to analyze interviews and large bodies of text. It has both free and premium plans.

LiGRE's most important analysis tools include the following:

• Automatic transcription (for transcribing both audio and video files).
• A survey builder.
• Data merging.
• A laboratory (a teaching platform where you can open your own qualitative research laboratory for a team).

• LiGRE Laboratory lets you teach your team about qualitative research.
• You can work with various media types, including books and articles.
• You can transcribe video and audio in more than 90 languages.

8. QDA Miner Lite

QDA Miner can analyze interviews, open-ended questions, and transcripts. Best of all, the user interface is easy and free.

QDA Miner can quickly analyze interviews, open-ended responses, journals, and still images. Plus, there are seven text search and retrieval tools, helping you reliably code your text in less time.

QDA Miner Lite also offers a Boolean text search tool. This gives you the unprecedented ability to retrieve and code text segments.

• QDA Miner Lite has an easy-to-use interface for coding, retrieving, and reviewing data. You can also present results in a variety of file formats.

Dedoose has qualitative and mixed method capabilities for anyone looking to analyze text, audio, images, videos, and surveys.

It claims to support traditional qualitative data management, coding, and analysis. Dedoose is a wise choice for analyzing raw consumer or market research data.

Dedoose is free for 30 days. Afterward, you can upgrade to a premium plan with rates adjusted depending on your needs or your group size.

• Dedoose encourages teamwork and collaboration.
• Dedoose presents information in interactive visuals, such as charts, plots, and tables for easy analysis.

10. Glimpse

Glimpse is ideal for customer success teams that want to analyze qualitative data related to consumer behaviors.

The easy-to-use tool comes with a qualitative sentiment analysis survey. This collects data from different sources and presents it in a straightforward format, whether chart, graphs, or heat maps.

• Information is organized or displayed concisely for easy analysis.
• You can collect information from various platforms and display them simultaneously.
• It identifies trends and patterns in products, industries, and companies.
• The software uses machine learning and what-if analysis tools to collect and present real-time data.

Choosing the Right QDAS

Ever since qualitative data analysis software was invented in the 80s, these tools have helped businesses harness data. For those in customer success, QDAS allows businesses to create memorable experiences for customers.

If you're looking for a QDAS to help you reach the right buyer, consider which tool aligns most with your business needs.

Editor's note: This post was originally published in June 2019 and has been updated for comprehensiveness.

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8 Great Tools To Perform Qualitative Data Analysis in 2022

Collecting qualitative customer data unlocks a potential goldmine of growth for your organization.

That is, if you know what to do with it. 

Qualitative data tells you how your customers feel and what they want from you. Examining your customer’s experience (CX) and putting the customer at the center of everything you do is likely to lead to an increase in your bottom line. 

However, in order to extract meaningful insights, you have to effectively analyze the data you collect. For this you’ll need the right qualitative data analysis tools.

Traditionally these tools were used exclusively by data specialists or analysts. Nowadays, however, data is so ubiquitous that even those outside of those remits can find themselves needing to make sense of large amounts of data. 

There are a lot of options out there and choosing the ideal software for your needs is not always easy. Here we’ll explain exactly what qualitative data analysis software is, then talk you through some of the best tools on the market. . 

What is Qualitative Data Analysis Software?

The 8 best qualitative data analysis software.

In order to understand what qualitative software can do for us, we need to start with what qualitative data actually is. 

Essentially, qualitative data is data that is non-numerical. It is descriptive and conceptual. Qualitative data is collected from a number of different sources. Some popular types include interviews, focus groups, surveys, e-mails, customer feedback, customer service tickets, observation notes, and phone calls.

This data, when it comes back to you, can be immense. Qualitative data analysis tools can help you organize, process, and analyze data for actionable insights. 

Qualitative data analysis software is used across a wide range of sectors and industries such as healthcare, the legal industry, e-commerce businesses, marketing departments - and everything in between. If your company has large amounts of data, you most likely need QDA software. 

The functionality of these tools varies greatly. At one end of the spectrum, you have software which allows you to tag and highlight important parts of your research. At the other end you have the fastest, most efficient kinds of software which employ the help of artificial intelligence to help you tag, analyze and visualize your research at record speed. 

Let’s jump straight into the ins and outs of 8 of the best qualitative data analysis tools out there.

Here is our list of the 8 top qualitative data analysis software. 

• MAXQDA - A well-established, reliable QDA Software 
• NVivo - Intuitive software offering some automation 
• ATLAS.ti - A powerful QA tool that offers some AI-improved functions 
• QDA Miner - Offers both a free and paid version 
• Quirkos - An easy to use, simplified tool 
• Dedoose - A tool that enables collaboration and team work
• Taguette - A free, open-source, data organization option 
• MonkeyLearn - AI-powered, qualitative analysis and visualization tool 

MAXQDA is a qualitative, quantitative, and mixed method data analysis tool. It lets you input data from a range of sources such as surveys, interviews, and focus groups to name a few. You can then tag and categorize this data for analysis. 

Best for:  In their words, MAXQDA was “created by researchers, for researchers.” This is across the education, non-profit and commercial sectors. 

Strengths: It’s easy to use and can support a number of different languages. It also uses AI to help users with audio transcription. 

This software was founded in 1989, so they have been around for a while and you can trust that their offerings are reliable. 

Weaknesses: Using it collaboratively in a team is not easy as individual users have to save their work and then merge the versions. This can be cumbersome. It’s also not the most attractive to look at compared with other software. 

Pricing: They have three different pricing plans that come with both an annual and perpetual price. They also offer a free trial. You can find more information here .

Like MAXQDA, NVivo is a software tool that allows its users to organize and store their qualitative data ready for analysis. You can also import word docs, PDFs, audio, images, and video. 

Best for: Researchers or academics looking for software with autocoding. 

Strengths: The interface is easy to use and is quite like Microsoft - this makes it instantly familiar and intuitive for many users. It’s much more powerful than some other offerings and offers automated transcription and autocoding. 

Weakness: NVivo struggles with languages that have characters, this isn’t a problem, for instance, for MAXQDA. 

While it is powerful compared to some of its competitors like Taguette, it doesn’t have the power to work with large data sets. After you’ve coded your data, you will still have to analyze your data manually, which could take a long time. 

Pricing: The price will vary according to a few factors, for example, whether you are a student or whether you are purchasing for an organization. More information can be found here.

3. ATLAS.ti

ATLAS.ti is a powerful QDA software tool, it supports large bodies of textual, graphical, audio and video data. Unlike other software in this category such as Quirkos, it has incorporated AI technology as it has evolved. 

Best for: This is best for research organizations, corporations, and academic institutions due to the extra AI features and the added cost.   

Strengths: Its interface is cleaner and sleeker than both Nvivo and MAXQDA, and collaboration is easier than in MAXQDA. It is also more powerful, boating both sentiment analysis and autocoding. 

Weakness: It can get expensive for individual users (such as students). Some users have also complained that the coding features are not that intuitive. 

Pricing: They offer a free trial and an extensive amount of licensing options based on sector and individual needs. More information can be found here.

4. QDA Miner

QDA Miner is a qualitative and mixed-method software that helps you to organize, code and analyze your data. They offer both a paid and free version called QDA Miner Lite.

Best for: Those looking for advanced visualizations and who are working alone.

Strengths: The latest version, QDA Miner 6, which was released in 2020, can link up with Tableau, a top data visualization tool , to give you an array of visualization options. 

Weakness:   QDA Miner doesn’t allow for collaboration which would be a big drawback if you are working on data with a team. Within the free version, the import and export functions are limited, as are the analysis functions. However, it might be enough to get you started or if you’re looking to test the waters. 

Pricing: They offer a number of packages according to sector and needs. Find details here.

Quirkos describes itself as a simple software tool that can help in the analysis of qualitative data. It is affordable and is popular within the education sector.  

Best for: Students and academics. 

Strengths: Quirkos offers a free trial which can be great if you are not sure what software is right for you. It’s also more affordable than some of the more advanced options like Atlas.ti. The drag and drop text functions make it simple and easy to use. You can also work collaboratively in Quirkos, and in real time.

Weakness: Its simplicity means that it offers less functionality. You have to code manually and this could be a deal-breaker if you have a lot of data. It also has fewer import options than other software. 

Pricing: There are three different options. Student, academic, commercial. Prices vary according to version and whether you want it cloud or offline. You can find more information here.

Dedoose is a 100% web-based tool for qualitative analysis. It was created by academics from UCLA and was designed to analyze both qualitative and quantitative data. It’s capable of importing data from a range of different formats, including documents, images, audio, video, and spreadsheets. 

Best for: Those who want a fully web based option where they can easily collaborate with team members.

Strengths: This software is team-oriented and user-friendly. It’s easy to import both text and visual data. It’s also compatible with mobile. 

Weakness: While they boast affordability, Dedoose can work out to be more expensive than other software as they only charge a monthly fee rather than a yearly license. The fact that it’s 100% web based may also be a negative for some. Like Quirkos, there is no AI or machine learning used in this tool. 

Pricing: Dedoose offers different prices depending on whether you’re an individual, student or group. More information can be found here.

7. Taguette

Taguette is a free open source qualitative data analysis tool that allows you to tag your data so you can then export it for analysis. 

Best for: Those looking for a basic, free option to organize their data for analysis. 

Strengths: It is very simple and easy to use. The fact that it’s open-source is also beneficial for many. It offers both an online and local version. 

Weakness: The fact that it is so simple means some drawbacks. Taguette doesn’t support images or video like some of the others. It also comes with zero automation compared to some of the bigger players and can’t analyze your data for you. 

Pricing: Taguette is free to use.

8. MonkeyLearn

MonkeyLearn is a powerful qualitative analysis software. It differs from most of the tools we have listed here in that it harnesses the power of AI and machine learning to make your data analysis process as efficient as possible. 

It offers an intuitive no-code interface that gets you to the stage of analyzing and visualizing your data in much less time. It’s also useful if you are not a data expert and you don’t have hours to spend coding manually or if, due to the size of your datasets, it’s simply impossible to do so. 

The MonkeyLearn Studio comes with pre-trained text analysis models, or, for more accurate insights, you can go ahead and build your own with your data and criteria. 

Once you’ve chosen your model you can start uploading your data from a range of options. Then you’ll be able to analyze this data with different tools like keyword extractor , feedback classifier , or sentiment analyzer . 

With that done, you can then view all your analysis in the interactive Studio dashboard (pictured below).

You can also learn more about our pricing and plans here . 

There are a number of qualitative data analysis software out there which will suit different needs. However, many of these tools require you to manually code your data in order to analyze it. 

If you are not that comfortable with coding or if you are working with datasets so large that this level of manual work is not feasible, you’ll need a tool like MonkeyLearn to help you process your qualitative data. 

MonkeyLearn provides a high level of automation, while still allowing you control of your data. This can make all the difference in terms of speed and cost. You can use your regained time to really understand the insights that crop up. 

Sign up for a free trial today to see how you can use MonkeyLearn Studio to best analyze your qualitative data.

Rachel Wolff

September 29th, 2021

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The Sheridan Libraries

• Qualitative Data Analysis Software (nVivo, Atlas.TI, and more)
• Sheridan Libraries

Qualitative Data Analysis Software (QDAS) overview

Choosing qda software, core qdas functions.

• Other QDAS Software
• Qualitative Data Sources

For direct assistance

Contact us , JHU Data Services   for assistance with access to nVivo and ATLAS.ti at the Data Services offices on A level, JHU Eisenhower Library.

Visit our website for more info and our upcoming training workshops !

Qualitative research has benefited from a range of software tools facilitating most qualitative methodological techniques, particularly those involving multimedia digital data. These guides focus on two major QDAS products, nVivo and ATLAS.ti.  Both programs can be found on the workstations at the Data Services computer lab on A-level, Eisenhower Library, and nVivo is available through JHU's SAFE Desktop . This guide also lists other QDA software and linked resources.

Many university libraries have produced comprehensive guides on nVivo, ATLAS.ti, and other QDA software, to which we will provide links with our gratitude

Schmider, Christian. n.d. What Qualitative Data Analysis Software Can and Can’t Do for You – an Intro Video . MERIT Library at the School of Education: School of Education, University of Wisconsin-Madison. Accessed January 7, 2020. https://www.youtube.com/watch?v=tLKfaCiHVic .

• Supported Methods
• Decision Factors
• Compare QDA Software

Qualitative Data Analysis (QDA) Software supports a variety of qualitative techniques and methodologies

Qualitative techniques supported by  QDAS

• Coding and Classifying
• Writing: analysis, description, memos
• Relating: finding and annotating connections, relationships, patterns
• Audio/Visual analysis: marking, clipping, transcribing, annotating
• Text mining: computer-aided discovery in large amounts of unstructured text
• Visualization: diagramming, relationship and network patterns, quantitative summary 

QDAS  supported methodologies

• Ethnography
• Case studies
• Grounded theory/ phenomenology
• Discourse/narrative analysis
• Sociolinguistic analysis
• Collaborative qualitative research
• Text analysis & text mining

Overview of qualitative methods from ATLAS.ti:  https://atlasti.com/qualitative-research-methods/

Decision factors for your research

• Methods to feature facilitation (in disciplinary context): How many features directly support your methodology?
• Interface for collection, analysis, reports: Do features accommodate most phases of your research workflow?
• Visualization and outputs: Does it produce and successfully export needed visualization without extensive modification?
• Cost and access to software: Is it worth the investment cost as well as in learning to use it? Look for education discounts.
• Software Comparisons: Commercial & Free. (George Mason University) Lists of flagship software, free software, and tools for converting codebooks among QDA software.
• QDA Software Comparison Chart (NYU Libraries) Comparison chart of QDA software from NYU Library's LibGuide
• Top 14 Qualitative Data Analysis Software Guide with descriptive summaries of the main QDA software, several with business focus.
• Dueling CAQDAS using ATLAS.ti and NVivo Webinar comparing features and use of ATLAS.ti and NVIvo for qualitative data analysis. Includes live demos.

Basic functions common to most QDA programs, and to NVivo and ATLAS.ti in particular:

• Application of a maintained set of terms and short phrases linked to segments of text or audio/video that can be queried and gathered for comparative analysis. 
• Longer narrative notes attached to text or a/v segments, or to codes
• Quick access to codes and segments that can be brought together in panel views for comparison, advanced Boolean search options, and flexible interlinking of segments, codes, and annotation
• Most QDAS facilitates transcribing audio and video, ideally maintaining the links between transcript and A/V segments. 
• Gathering codes, segments, and annotations facilitates pattern discovery and further description of relationships. Some QDAS support social network analysis techniques and visualization
• A range of reports using queries and filters to assemble data and annotations facilitates analysis and writing results.
• ​ Typically includes code tables, social network graphs, and annotated A/V clips.
• Shared access to data & analysis, facilitating comments and discussion, and tracking contributor actions and changes.
• Next: NVivo >>
• Last Updated: Apr 25, 2024 3:54 PM
• URL: https://guides.library.jhu.edu/QDAS

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Top 8 Online Qualitative Research Tools for Business Success

Have you ever wondered how businesses truly understand their customers beyond the numbers and statistics? The answer lies in online qualitative research tools, the storytellers of the business world.

When it comes to understanding the world around us, numbers don’t always tell the whole story. That’s where qualitative research tools step in. These tools are the guiding lights for researchers, instrumental in collecting, analyzing, and interpreting non-numerical data.

It is like having a conversation to understand what someone thinks or feels truly. It moves beyond numbers, focusing on words and experiences to reveal the rich and interesting parts of life.

In this blog, we will explore the top 8 qualitative research tools that will help you to analyze your qualitative data in the best way.

What is Qualitative Research?

Qualitative research is a way of learning about customers and their stories. Instead of using numbers, like counting or measuring, it focuses on words and experiences. It’s like having a conversation to really understand what a customer thinks or feels. 

This kind of research helps us see the rich and interesting parts of life that can’t be captured by just looking at numbers. This approach involves various online qualitative research methods such as interviews, online focus groups, observations, and content analysis. These methods explore the depth of user experiences, providing an excellent understanding beyond numerical data.

What are Qualitative Research Tools?

Qualitative research tools are instruments or methods used to collect, analyze, and interpret non-numerical data in qualitative research. These tools help researchers explore and understand the richness and complexities of human experiences, behaviors, and social phenomena.

These tools are chosen based on the research objectives, the nature of the data, and the preferred methods for gathering and analyzing qualitative information. Researchers often use a combination of these tools to gain a comprehensive understanding of the subject under investigation.

Why Do You Need Qualitative Research Tools?

Qualitative research tools play a crucial role in gaining deeper insights into human experiences, behaviors, and social phenomena. Here’s why you need qualitative research tools:

Diving into the Details

Quantitative research might tell you how many people like a new gadget, but it won’t reveal why. Qualitative research tools, like interviews and an online focus group. It allows researchers to dive deep into the details. It’s like turning on a spotlight in a dim room, illuminating the ‘whys’ and ‘hows’ behind people’s thoughts and actions.

Getting Personal

Numbers can be cold and impersonal. These tools bring warmth to the process by engaging directly with people. Through methods like interviews, researchers create a space for participants to share their unique perspectives. It provides a personal touch to the data.

Context is Key

Imagine trying to understand a joke without knowing the context – it just wouldn’t make sense. Qualitative tools emphasize the importance of context. Whether through observations or participant diaries, these tools help researchers study people in their natural settings, capturing the context that shapes their experiences.

Flexibility in Exploration

Life is complex and ever-changing, and so is qualitative analysis. These tools offer flexibility, allowing researchers to adapt their approach as they learn more. It’s like adjusting the sails on a ship to navigate through uncharted waters. It ensures the research stays relevant and insightful.

Making Sense of the Story

Life is a story, and qualitative research tools help researchers read between the lines. Techniques like coding and thematic analysis help make sense of the narrative. It identifies patterns and themes that might be easily overlooked with purely quantitative methods.

Turning Data into Insights

These tools aren’t just about collecting data; they’re about turning that data into meaningful insights. By employing structured methods, researchers enhance the validity and reliability of their findings. It ensures that the conclusions drawn are robust and trustworthy.

Features of A Good Qualitative Research Software

Having the right tools is like having a trustworthy guide on a journey. When it comes to software, there are certain features that make the exploration of data smoother and more insightful. Here, let’s unravel the key features of good qualitative data analysis software, making your research journey more efficient and enjoyable.

User-Friendly Interface

A user-friendly interface makes navigation smooth, allowing researchers to focus on their study rather than getting lost in a complex tool. Simple menus, clear icons, and intuitive design are the markers of user-friendly software.

Data Organization and Management

Like folders keep your documents tidy, good qualitative research software helps organize and manage your data effectively. It should offer features like easy categorization, tagging, and sorting, making it a breeze to locate specific information within your sea of data.

Powerful Search Capabilities

Imagine trying to find a needle in a haystack without a magnet. Similarly, good qualitative research software should provide powerful search capabilities. Researchers should be able to quickly locate specific keywords, phrases, or themes, saving time and effort.

Support for Various Data Types

It involves diverse data types – from text and audio to images and videos. Good software should be versatile, supporting multiple data formats. This ensures that no matter what type of data you gather, the software can handle and analyze it effectively.

Coding and Analysis Tools

Coding is like decoding a secret message within your data. Good qualitative research software provides robust coding and analysis tools. Researchers should be able to apply codes to segments of data, identify patterns, and extract meaningful insights, making the analysis process more systematic and efficient.

Collaboration Features

Research is often a team effort, and collaboration is key. Good software should facilitate collaboration among researchers, allowing them to work on the same project seamlessly. Features like shared access, comments, and version control contribute to a smoother collaborative experience.

Compatibility and Integration

A good qualitative research software plays well with others. It should be compatible with various operating systems and integrate smoothly with other tools commonly used in the research process. This ensures a seamless workflow without unnecessary hurdles.

Top 8 Online Qualitative Research Tools

Whether you’re a seasoned researcher or just dipping your toes into the world of online qualitative research, these top eight tools are here to guide you through the digital landscape.

01. QuestionPro

QuestionPro is more than just surveys. It’s a comprehensive platform for creating both qualitative and quantitative research needs. From versatile survey design to real-time analytics, QuestionPro has your research journey covered.

Best Features:

• Coding and Analysis: Robust coding features for in-depth analysis.
• Interactive Reporting: Visualize data through charts and graphs.
• Sentiment Analysis: Understand participant sentiments in responses.
• Mixed-Methods Analysis: Supports both qualitative and quantitative data analysis.
• Real-time Analytics: Obtain instant insights with live tracking.
• Easy to use, even for beginners.
• Versatile survey options for qualitative research.
• Affordable pricing plans.
• Robust analytics and reporting features.
• Good customer support.
• Pricing may be higher for some users.

NVivo is a qualitative data analysis powerhouse. It organizes, analyzes, and extracts insights from diverse data sources. It makes it a go-to choice for researchers delving deep into qualitative exploration.

• Visualization Tools: Create visual models to understand complex relationships.
• Team Collaboration: Collaborate with team members in real-time.
• Coding and Analysis : Robust coding features for in-depth analysis .
• Powerful and comprehensive data analysis capabilities.
• Extensive training resources are available.
• Excellent customer support.
• Regular updates and improvements.
• Compatibility issues with certain file types.

03. Qualtrics

Qualtrics is a versatile online survey and market research tool used for quantitative and qualitative research needs. Automated analysis and multimedia integration make it a one-stop shop for researchers.

• Survey Building: Easily create surveys with a drag-and-drop interface.
• Automated Analysis: Advanced analytics tools for insightful results.
• Multimedia Integration: Include various media types in your surveys.
• Panel Management: Access a diverse panel of participants for research.
• Real-time Reporting: Track responses and insights in real-time.
• Versatility in survey design.
• Extensive customization options.
• Robust security measures.
• Active community and support resources.
• Advanced features may require training.

04. Thematic

Thematic stands out with its AI-driven analysis tools. It helps researchers uncover patterns and sentiments within qualitative data. It’s an efficient and cost-effective choice for those embracing automation.

• Automated Coding: AI-driven coding for efficiency.
• Interactive Visualization: Visual representation of themes and patterns.
• Collaboration Features: Share findings and collaborate with team members.
• Efficient Data Processing: Speeds up the data analysis process.
• Quick and efficient analysis with AI capabilities.
• No need for extensive coding knowledge.
• Real-time collaboration features.
• Cost-effective compared to some alternatives.
• Limited customization options.

05. FocusVision

FocusVision offers a comprehensive suite of online qualitative research tools, including live video interviews, discussion boards, and advanced analytics. It’s a solution for researchers looking for diverse qualitative methods.

• Live Video Interviews: Conduct real-time video interviews.
• Discussion Boards: Facilitate asynchronous discussions.
• Survey Integration: Combine quantitative and qualitative methods.
• Participant Engagement: Tools to keep participants engaged.
• Comprehensive suite for various online methods.
• Engaging and interactive tools.
• Real-time collaboration with participants.
• Limited customization in discussion boards.

06. Discuss.io

Discuss.io specializes in video interviews and qualitative discussions with participants worldwide. It’s a global platform for qualitative insights with features like live interviews, translation services, and real-time collaboration.

• Live Video Interviews: Conduct one-on-one or group interviews.
• Translation Services: Supports interviews in multiple languages.
• Screen Sharing: Participants can share screens for more interactive discussions.
• Real-time Collaboration: Collaborate with team members during interviews.
• Advanced tools for efficient analysis.
• Secure and compliant with privacy regulations.
• Some users report occasional platform glitches.

Delve is a qualitative research platform emphasizing collaboration, data synthesis, and insights generation. It’s designed for teams working together to derive meaningful insights from qualitative data.

• Project Collaboration: Facilitates collaboration among team members.
• Data Synthesis: Gathers and synthesizes data from various sources.
• Visualization Tools: Create visual representations of data.
• Centralized Data Repository: All data is stored in one accessible location.
• Streamlined collaboration for research teams.

08. ATLAS.ti

ATLAS.ti is a robust qualitative research tool that focuses on data management, coding, and analysis. It provides a structured environment for researchers to navigate and analyze qualitative data.

• Powerful Coding Tools: Robust tools for coding and analysis.
• Data Visualization: Visual representations of coded data.
• Mixed-Methods Support: Suitable for both qualitative and quantitative data.
• Limited in-built survey capabilities.
• Large datasets may slow down performance.

Why Choose QuestionPro as the best online qualitative research tool?

Choosing the best online qualitative research tool involves considering a range of factors that align with your specific research needs. QuestionPro stands out among its peers for several compelling reasons, making it a strong choice for researchers:

Versatile Survey Options for Varied Needs

Your research questions are unique, and QuestionPro gets that. Choose from a range of survey options, tailor-made for both qualitative and quantitative needs. It’s about flexibility that aligns with your research goals.

Responsive Support When You Need It

Stuck in a research roadblock? QuestionPro has your back with responsive customer support. Get the help you need when you need it, ensuring that your research journey stays smooth.

Robust Analytics for Insightful Interpretations

Your data deserves more than a glance. With QuestionPro, dive deep into your qualitative data using advanced analytics and reporting features. It’s about extracting meaningful insights that go beyond the surface.

Affordable Plans for Every Budget

Research doesn’t have to break the bank. QuestionPro offers pricing plans that create various budgets, ensuring that research is accessible to all, be it a seasoned researcher or a student on a tight budget.

Visual Storytelling with Interactive Reporting

Numbers and text can only say so much. QuestionPro stands out with interactive reporting features that turn your qualitative data into engaging charts and graphs, helping you tell a compelling visual story.

Versatility That Speaks Your Language

QuestionPro goes beyond the usual survey tools. It’s designed for the unique demands of this research, allowing you to create surveys that dive deep into participant experiences.

The blog has explored the top eight online qualitative research tools, each offering unique features to enhance the analytical process. The importance of these tools lies in their ability to bring a personal touch to the research process.

Out of all the choices, QuestionPro stands out as a great option. It offers different types of surveys, helpful customer support, powerful data analysis, budget-friendly plans, and features that make reports interactive. 

QuestionPro platform is versatile, which means it understands what researchers need, especially in qualitative research. So, contact QuestionPro if you are thinking of conducting qualitative research for your business!

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All-in-one Qualitative Coding Software

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Elevate your qualitative research with cutting-edge Qualitative Coding Software

MAXQDA is your go-to solution for qualitative coding, setting the standard as the top choice among Qualitative Coding Software. This powerful software is meticulously designed to accommodate a diverse array of data formats, including text, audio, and video, while offering an extensive toolkit tailored specifically for qualitative coding endeavors. Whether your research demands data categorization, thematic visualization, mixed-methods analysis, or quantitative content examination, MAXQDA empowers you to seamlessly uncover the profound insights crucial for your qualitative research.

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Your analysis.

Revolutionize Your Research: Unleash the Power of Qualitative Coding Software

Qualitative coding software is an essential companion for researchers and analysts seeking to delve deeper into their qualitative data. MAXQDA’s user-friendly interface and versatile feature set make it the ideal tool for those embarking on qualitative coding journeys. Its capabilities span across various data types, ensuring you have the tools required to effectively organize, analyze, and interpret your qualitative data.

Developed by and for researchers – since 1989

Having used several qualitative data analysis software programs, there is no doubt in my mind that MAXQDA has advantages over all the others. In addition to its remarkable analytical features for harnessing data, MAXQDA’s stellar customer service, online tutorials, and global learning community make it a user friendly and top-notch product.

Sally S. Cohen – NYU Rory Meyers College of Nursing

Qualitative Coding is Faster and Smarter with MAXQDA

MAXQDA makes qualitative coding faster and easier than ever before. Code and analyze all kinds of data – from texts to images and audio/video files, websites, tweets, focus group discussions, survey responses, and much more. MAXQDA is at once powerful and easy-to-use, innovative and user-friendly, as well as the only leading qualitative coding software that is 100% identical on Windows and Mac.

As your all-in-one Qualitative Coding Software, MAXQDA can be used to manage your entire research project. Easily import a wide range of data types such as texts, interviews, focus groups, PDFs, web pages, spreadsheets, articles, e-books, bibliographic data, videos, audio files, and even social media data. Organize your data in groups, link relevant quotes to each other, make use of MAXQDA’s wide range of coding possibilities for all kind of data and for coding inductively as well as deductively. Your project file stays flexible and you can expand and refine your category system as you go to suit your research.

Qualitative coding made easy

Coding qualitative data lies at the heart of many qualitative data analysis methods. That’s why MAXQDA offers many possibilities for coding qualitative data. Simply drag and drop codes from the code system to the highlighted text segment or use highlighters to mark important passages, if you don’t have a name for your category yet. Of course, you can apply your codes and highlighters to many more data types, such as audio and video clips, or social media data. In addition, MAXQDA permits many further ways of coding qualitative data. For example, you can assign symbols and emojis to your data segments.

Tools tailor made for coding inductively

Besides theory-driven qualitative data analysis, MAXQDA as an all-in-one qualitative coding software strives to empower researchers that rely on data-driven approaches for coding qualitative data inductively. Use the in-vivo coding tool to select and highlight meaningful terms in a text and automatically add them as codes in your code system while coding the text segment with the code, or use MAXQDA’s handy paraphrase mode to summarize the material in your own words and inductively form new categories. In addition, a segment can also be assigned to a new (free) code which enables researchers to employ a Grounded Theory approach.

Organize your code system

When coding your qualitative data, you can easily get lost. But with MAXQDA as your qualitative coding software, you will never lose track of the bigger picture. Create codes with just one click and apply them to your data quickly via drag & drop. Organize your code system to up to 10 levels and use colors to directly distinguish categories. If you want to code your data in more than one perspective, code sets are the way to go. Your project file stays flexible and you can expand and refine your category system as you go to suit your research.

Further ways of coding qualitative data

MAXQDA offers many more functionalities to facilitate the coding of your data. That’s why researchers all around the world use MAXQDA as their qualitative coding software. Select and highlight meaningful terms in a text and automatically add them as codes in your code system, code your material using self-defined keyboard shortcuts, code a text passage via color coding, or use hundreds of symbols and emoticons to code important text segments. Search for keywords in your text and let MAXQDA automatically code them or recode coded segments directly from the retrieved segments window. With the unique Smart Coding tool reviewing and customizing your categorization system never has been this easy.

Creative coding

Coding qualitative data can be overwhelming, but with MAXQDA as your qualitative coding software, you have an easy-to-use solution. In case you created many codes which in hindsight vary greatly in their scope and level of abstraction, MAXQDA is there to help. Creative coding effectively supports the creative process of generating, sorting, and organizing your codes to create a logical structure for your code system. The graphic surface of MAXMaps – MAXQDA’s tool for creating concept maps – is the ideal place to move codes, form meaningful groups and insert parent codes. Of course, MAXQDA automatically transfers changes made in Creative Coding Mode to your Code System.

Visualize your qualitative coding and data

As an all-in-one Qualitative Coding Software, MAXQDA offers a variety of visual tools that are tailor-made for qualitative research. Create stunning visualizations to analyze your material. Of course, you can export your visualizations in various formats to enrich your final report. Visualize the progression of themes with the Codeline, use the Word Cloud to explore key terms and the central themes, or make use of the graphical representation possibilities of MAXMaps, which in particular permit the creation of concept maps. Thanks to the interactive connection between your visualizations with your MAXQDA data, you’ll never lose sight of the big picture.

AI Assist: Qualitative coding software meets AI

AI Assist – your virtual research assistant – supports your qualitative coding with various tools. AI Assist simplifies your work by automatically analyzing and summarizing elements of your research project and by generating suggestions for subcodes. No matter which AI tool you use – you can customize your results to suit your needs.

Free tutorials and guides on qualitative coding software

MAXQDA offers a variety of free learning resources for qualitative coding, making it easy for both beginners and advanced users to learn how to use the software. From free video tutorials and webinars to step-by-step guides and sample projects, these resources provide a wealth of information to help you understand the features and functionality of MAXQDA as qualitative coding software. For beginners, the software’s user-friendly interface and comprehensive help center make it easy to get started with your data analysis, while advanced users will appreciate the detailed guides and tutorials that cover more complex features and techniques. Whether you’re just starting out or are an experienced researcher, MAXQDA’s free learning resources will help you get the most out of your qualitative coding software.

Free MAXQDA Trial for Windows and Mac

Get your maxqda license, compare the features of maxqda and maxqda analytics pro, faq: qualitative coding software.

When it comes to qualitative coding software, MAXQDA stands out as a top choice for researchers. MAXQDA is a comprehensive qualitative data analysis tool that offers a wide range of features designed to streamline the coding process and assist researchers in making sense of their qualitative data.

MAXQDA’s user-friendly interface and robust set of tools make it a reliable and powerful option for qualitative coding tasks, making it a popular choice among researchers.

One highly recommended software tool for coding qualitative data is MAXQDA. MAXQDA provides researchers with a set of tools for analyzing and interpreting their qualitative data, making it an excellent choice for qualitative coding tasks.

MAXQDA offers a range of features, including text analysis and data visualization, making it a comprehensive solution for qualitative data analysis.

Coding qualitative data involves systematically categorizing and labeling segments of your data to identify themes, patterns, and trends. MAXQDA simplifies this process by providing an intuitive interface and tools specifically designed for qualitative coding tasks.

To code qualitative data with MAXQDA, you typically follow these steps:

• Import your qualitative data into MAXQDA, such as interview transcripts, survey responses, or text documents.
• Read through your data to gain a deep understanding of the content.
• Identify keywords, phrases, or themes relevant to your research objectives.
• Create codes in MAXQDA to represent these keywords, phrases, or themes.
• Apply the created codes to specific segments of your data by highlighting or selecting the relevant text.

MAXQDA’s flexibility and organization features make it an excellent choice for coding qualitative data efficiently and effectively.

Qualitative coding methods are techniques used to analyze and categorize qualitative data. These methods help researchers make sense of the data and identify key themes, patterns, and insights. MAXQDA supports various qualitative coding methods, making it a versatile tool for researchers.

Some common qualitative coding methods include:

• Thematic Coding: This involves identifying and categorizing recurring themes or topics in the data.
• Content Analysis: Researchers analyze the content of the data to understand its meaning and context.
• Grounded Theory: A systematic approach to developing theories based on the data itself.
• Framework Analysis: A method for structuring and analyzing large amounts of qualitative data.
• Constant Comparative Analysis: Comparing new data with existing data to refine codes and categories.

MAXQDA’s tools and features are designed to support these coding methods, allowing researchers to choose the approach that best suits their research goals.

Qualitative coding is the process of systematically analyzing and categorizing qualitative data to identify patterns, themes, and insights. It involves assigning codes or labels to specific segments of qualitative data, such as interview transcripts, survey responses, or text documents. These codes help researchers organize and make sense of the data, facilitating data interpretation and the extraction of meaningful information.

MAXQDA is a valuable tool for qualitative coding as it provides researchers with the means to create, apply, and manage codes efficiently, allowing for a more structured and rigorous analysis of qualitative data.

For Mac users looking for qualitative coding software, MAXQDA is an excellent choice. MAXQDA offers a Mac version of its software that is fully compatible with macOS, providing Mac users with a seamless qualitative data analysis experience.

With MAXQDA for Mac, researchers can take advantage of all the features and capabilities that make MAXQDA a top choice in qualitative coding software. Whether you’re conducting research on a Mac computer or prefer the Mac environment, MAXQDA is a reliable and efficient solution.

For students venturing into qualitative research, MAXQDA is an ideal qualitative coding software choice. MAXQDA offers a user-friendly interface and a range of resources designed to support students in their research journey. It provides academic licenses at affordable prices, making it accessible to students on a budget.

MAXQDA’s intuitive design and comprehensive features empower students to code, analyze, and interpret qualitative data effectively. It also offers educational resources and tutorials to help students get started with qualitative research and coding.

Qualitative coding software, such as MAXQDA, offers a range of key features that are essential for effective qualitative data analysis. Some of the key features of qualitative coding software include:

• Code Management: The ability to create, organize, and manage codes for data segmentation.
• Data Import: The capability to import various types of qualitative data, including text, audio, and video files.
• Annotation Tools: Tools for adding comments, annotations, and notes to the data for context and analysis.
• Data Visualization: Graphs, charts, and visual aids to represent and explore data patterns.
• Search and Retrieval: Efficient search functions to locate specific data segments or codes within large datasets.
• Collaboration Tools: Features for collaborative coding and analysis with team members.
• Reporting and Export: The ability to generate reports, export data, and share findings with others.

MAXQDA excels in offering these features and more, making it a comprehensive solution for qualitative coding and analysis.

Qualitative coding software, like MAXQDA, plays a crucial role in assisting researchers with qualitative data interpretation. Here’s how:

1. Structure and Organization: Coding software helps researchers organize their qualitative data into manageable segments by assigning codes and categories. This structured approach facilitates easier data interpretation by breaking down complex information into meaningful units.

2. Pattern Recognition: By coding and categorizing data, researchers can quickly identify patterns, trends, and recurring themes. MAXQDA’s tools allow for easy visualization of these patterns, aiding in data interpretation.

3. Cross-Referencing: Qualitative coding software allows researchers to cross-reference data segments, codes, and categories. This cross-referencing helps in exploring relationships and connections within the data, leading to deeper insights.

4. Collaboration: Collaborative coding and analysis tools in software like MAXQDA enable researchers to work together, share interpretations, and refine their understanding of the data collectively.

In summary, qualitative coding software streamlines the process of data interpretation by providing tools and features that enhance the researcher’s ability to uncover meaningful insights from qualitative data.

Yes, qualitative coding software, including MAXQDA, is suitable for both beginners and experienced researchers. MAXQDA is known for its user-friendly interface, making it accessible to those who are new to qualitative research and coding.

For beginners, MAXQDA provides educational resources and tutorials to help them get started with qualitative data analysis. It offers a gentle learning curve, allowing novice researchers to quickly grasp the essentials of coding and analysis.

Experienced researchers benefit from MAXQDA’s advanced features and capabilities. It offers a robust set of tools for in-depth analysis, data visualization, and complex coding tasks. Researchers with extensive experience can leverage these features to enhance the rigor and depth of their qualitative research.

In essence, MAXQDA caters to researchers at all levels, making it a versatile choice for qualitative coding.

Qualitative coding can be done without software, but it can be a more time-consuming and labor-intensive process. When coding without software, researchers typically rely on manual methods such as highlighting, underlining, or physically tagging segments of printed text.

However, using qualitative coding software like MAXQDA offers several advantages. It streamlines the coding process, provides tools for efficient organization and retrieval of coded data, and offers features like data visualization and collaboration. These benefits can significantly enhance the quality and efficiency of qualitative coding.

While it’s possible to code qualitatively without software, utilizing a dedicated tool like MAXQDA can save researchers time and effort and lead to more rigorous and comprehensive data analysis.

Conducting Qualitative Research Online: Challenges and Solutions

• Practical Application
• Open access
• Published: 11 June 2021
• Volume 14 , pages 711–718, ( 2021 )

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• Stacy M. Carter   ORCID: orcid.org/0000-0003-2617-8694 1 ,
• Patti Shih   ORCID: orcid.org/0000-0002-9628-7987 1 ,
• Jane Williams   ORCID: orcid.org/0000-0002-0142-0299 2 ,
• Chris Degeling   ORCID: orcid.org/0000-0003-4279-3443 1 &
• Julie Mooney-Somers   ORCID: orcid.org/0000-0003-4047-3403 2  

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What ways of thinking and concrete strategies can assist qualitative health researchers to transition their research practice to online environments? We propose that researchers should foreground inclusion when designing online qualitative research, and suggest ethical, technological and social adaptations required to move data collection online. Existing research shows that this move can aid in meeting recruitment targets, but can also reduce the richness of the data generated, as well as how much participants enjoy participating, and the ability to achieve consensus in groups. Mindful and consultative choices are required to prevent these problems. To adapt to ethical challenges, researchers should especially consider participant privacy, and ways to build rapport and show appropriate care for participants, including protocols for dealing with distress or disengagement, managing data, and supporting consent. To adapt to technological challenges, research plans should choose between online modalities and platforms based on a clear understanding of their particular affordances and the implications of these. Finally, successful research in virtual social environments requires new protocols for engagement before data collection, attention to group numbers and dynamics, altered moderator teams and roles, and new logistical tasks for researchers. The increasing centrality of online environments to everyday life is driving traditional qualitative research methods to online environments and generating new qualitative research methods that respond to the particularities of online worlds. With strong design principles and attention to ethical, technical and social challenges, online methods can make a significant contribution to qualitative research in health.

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Qualitative Research: Ethical Considerations

How to use and assess qualitative research methods

Questionnaire Design

Avoid common mistakes on your manuscript.

Interviewer: Now, you were just about to say something when you froze.

Participant: Yeah …

Interviewer: Oh, now you’re freezing again.

Participant: Let me just close this other …

Interviewer: No, I’ve got you again, that’s, you’re coming back.

Participant: Ok, good, I just closed a window I had open.

Interviewer: Just give me one second and I’ll just shout upstairs at my daughter who is probably watching something.

Participant: Ok.

Interviewer: (has conversation with daughter) Sorry about that.

Participant: That’s ok. It’s part of, part of the world we live in.

Interviewer: It is. The cat’s been trying to come and have a look at you as well, but I’ve managed to keep her down.

Excerpt from qualitative interview conducted on a videoconferencing platform in 2020

Many readers will recognise the encounter above, and may have had interactions like it, attempting to balance the personal and the professional, attempting to transpose rules and norms of one milieu into another, attempting to connect against distraction and technological difficulties. These issues are perhaps more acute for research interactions—like the one above—than for everyday interactions. In research, the need to generate meaningful findings, the requirements of human research ethics, and limits of time and resources increase the stakes. The challenge is arguably greater still for qualitative research, where participants are asked to speak in depth about often very personal, private or challenging issues, and rapport and support for participants can be critical to success. Our aim here is to provide practical assistance to help qualitative researchers and participants succeed in this online terrain.

Qualitative methods are a natural fit for patient-centred outcomes and health preferences research, as they allow the study of participants’ experiences, choices and actions from the participant’s perspective. While qualitative methods are often used as a preliminary step in the development of quantitative instruments or studies [ 1 ], qualitative studies provide complex and patient-centred insights in their own right [ 2 ], and are now commonly synthesised to inform health policy, health services, and health technology assessment [ 3 ]. Qualitative health researchers are increasingly turning to online platforms to collect data, whether in response to social distancing requirements during the COVID-19 pandemic [ 4 ], to research online worlds as unique cultures and communication environments [ 5 ], or because innovative methods can achieve novel aims [ 6 ]. Moving research online is not a simple ‘like-for-like’ transfer however; the transition can be a disorienting struggle even for experienced researchers.

Qualitative research is diverse and heterogeneous, with different underpinning assumptions, aims, methods for data collection and analysis, and reporting styles [ 7 ]. We will concentrate only on interview and focus group methods because they are frequently used in patient preferences research. The online environment is reinventing these methods, with adaptations including online focus groups, email interviewing, Instant Messaging (IM) interviewing, and the use of internet-based video interviewing [ 8 ]. There are many other qualitative methods that can be used in the online environment, including netnography [ 9 ], online visual research methods [ 10 ], and social media research methods [ 5 ], but these are beyond the scope of this paper.

Qualitative researchers have adapted repeatedly to technological change, both in the mode of engagement with participants, and the collection, transformation and storage of data. A longitudinal view reveals multiple moments of technological recalibration for qualitative researchers. For some time, researchers accustomed to face-to-face interviews asked whether telephone interviews were acceptable, but they are now both commonplace and recognised as highly suitable for interaction with certain participants, e.g. with elites [ 11 , 12 ]. As natural language processing improves and data storage and processing speed increases, human transcribers are being replaced with automated transcription software, and transcripts with clipping and coding digital recordings directly [ 13 ]. These changes have not been linear—technologies are reinvented and recombined over time—but change and technological adaptation have been a constant. In each of these transformations, new issues arise that need to be considered.

The authors are experienced qualitative researchers who share an interest in methodology, methods and research ethics. This paper emerged through discussion of issues that had arisen in our online experience to date and potential issues we could foresee given the different topics and specific populations we research, along with looking to the literature for answers to questions we faced in our practice. We are writing in early 2021, when social distancing requirements in many countries have greatly accelerated a nascent move towards greater online data collection. As the qualitative research community continues to come to terms with these changes, we consider the opportunities and challenges of online data collection that pandemic conditions have made evident.

1 Doing Qualitative Research in a Virtual Environment: Opportunities, Challenges and Solutions

A recent scoping review compared face-to-face with online research studies of health and illness experiences. The authors concluded that while online methods appear to increase the likelihood of obtaining the desired sample, responses are typically shorter, less contextual information is obtained, and relational satisfaction and consensus development are lower [ 14 ]. This does not mean that online methods are inferior, but it does mean that researchers should deliberately plan to mitigate their potential weaknesses.

In the following sections, we consider a set of interconnected issues, taking a lead from Davies and colleagues’ scoping review [ 14 ]. First, we will argue that while the online environment may facilitate participation, the move online can enable or hinder inclusion. We will then consider the ethical, technological and social adaptations required in online data collection to, among other things, maximise data quality and care for participants. We note as a background premise that usual qualitative study design considerations—the need for sound aims, research questions, recruitment and sampling strategies, interview or focus group guides and analysis strategies—still hold. We will focus on adaptation of procedures, with sound research design principles assumed [ 15 , 16 ].

2 Moving Online Can Enable or Hinder Inclusion

Unjustly excluding people because of their technological or material circumstances is an old research ethics problem that potentially takes a new form in online research, potentially altering the accessibility of research for participants in positive or negative ways. Transitioning from face-to-face to online data collection can broaden access by lifting geographic limits. Online data collection can reduce the burdens of time and cost of participating in research. Participants do not have to travel or host a researcher, and it may be more convenient to conduct interviews and focus groups outside of working hours. These adjustments are likely to make participation easier or more appealing for some groups that previously faced practical limitations to taking part in qualitative research. For example, people with limited mobility, as well as caregivers, may find online participation from home inviting because they do not need to make the same sorts of accommodations that can stand in the way of in-person research [ 17 ].

Conversely, online data collection may also limit participation only to those who have a web-enabled device, and sometimes authority to install software. Online video platforms require a good-quality internet connection and relatively high data usage. People without access to fast and reliable internet, as well as people with limited access to data, may find it difficult or less appealing to participate. Online data collection risks excluding, or creating additional burdens and considerable stress for, participants who do not feel competent in the use of technology. Finally, not all technology can accommodate the needs of participants living with specific disabilities.

Researchers can mitigate these barriers to participation and inclusion through mindful and consultative technological and logistical choices. For those with limited access to technology, video conferencing platforms may be inappropriate; inclusion may require conducting an interview without video (audio only) or via telephone to reduce the need for a high-quality internet connection. Researchers may also consider methods such as email interviewing or IM interviewing, which offer accessibility benefits (e.g. more time for participant reflection, less data-intensive technology) but also disadvantages (e.g. requires sufficient literacy) [ 8 ]. Researchers can provide participants with data credit vouchers so that they can participate in video calls without the burden of additional data costs. Different platforms offer different participation options for people with disabilities (Table 1), and accessibility options are improving. Accessibility experts and advocacy groups are a good source of information (e.g. [ 18 , 19 ]). As in face-to-face data collection, specialist advice, including from participants themselves, can assist inclusion of people who use augmentative or alternative communication devices. Researchers should also be flexible with, and take the lead from, participants to maximise inclusion, as participants may have identified or developed solutions that make video conferencing platforms more accessible for them. People with impaired hearing, for example, may find it difficult to rely on lip-reading in video calls, but could participate via a synchronous text chat interview, or on a video platform with the right speech-to-text captioning tool, or with a sign language interpreter pinned next to the main speaker on screen [ 20 , 21 , 22 ].

Traditionally, meeting in person has helped shape sampling and recruitment strategies for studies. The location of the research team has often determined the geographic parameters of the study population because face-to-face interviews and focus groups have been the norm for data collection. Online platforms potentially eradicate some geographic barriers and may prompt researchers to think differently about their research questions. While it may be tempting to substantially widen sampling and recruitment because online methods have made it possible, researchers should remain mindful of the importance of methodological concerns. Study populations are shaped by considerations other than practicality. Researchers must be clear about why they have identified the population of interest and how that sample will help them answer their study questions. It may be that geographic location or experience of a particular healthcare system remains an important factor to capture.

3 Practical Ways to Adapt to Technological, Social and Ethical Challenges in Online Research

Successful online data collection requires three kinds of adaptation: to ethical challenges, to a new technological environment, and to a new social environment. These are interconnected but for clarity we deal with each of them in turn below.

3.1 Adapting to Ethical Challenges

In addition to usual research ethics considerations, online data collection raises special challenges. For example, online data collection creates different privacy risks. Online engagement with video means a researcher (and if a focus group, other group members) can potentially see and hear a participant’s domestic space. There are other privacy considerations—some communication platforms require a participant profile, including name, date of birth, email address and/or mobile phone number; participants may not want a profile, or if they have one they may not want to disclose it. Supporting people to participate anonymously may be vital for some populations/research topics. Participants also need access to a quiet and private space. For example, participants who rely on public libraries for internet access are unlikely to be able to do this with privacy.

During in-person research, we use ordinary actions to show our presence and care, or to create rapport: small talk, sharing a beverage, handing a tissue to a distressed participant, closing an encounter by walking a participant out of the building. Online data collection means the loss of this embodied care. Researchers need to develop strategies to establish rapport or comfort a distressed participant; these protocols should be included in ethics applications. We suggest the following adaptations to address these and other important ethical concerns.

Develop a protocol for dealing with distress or disengagement Common in research with vulnerable participants or on sensitive topics, we recommend these protocols for all online qualitative research. Develop clear strategies for how you will deal with an interview participant who becomes visibly distressed or unresponsive, moves away from the screen, shuts down the platform, does not return from an agreed comfort break, or where you witness problematic interactions with other people in the participant’s setting. A similar protocol is advisable for focus groups to deal with distress, or with abuse or discriminatory actions between participants. Ensure you have an alternative means to contact each participant and let participants know in advance under what circumstances you will contact them via this alternative channel.

Ensure video and/or audio recordings are stored appropriately Researchers should check where an online platform is storing recordings and their privacy policy. Using a platform’s cloud service can be in contravention of local privacy legislation (e.g. the European Union’s General Data Protection Regulation [GDRP]) or ethical approval; choose a platform that allows researchers to store recordings on their computer or institutional cloud service. For sensitive research topics, recording via an offline audio device (e.g. digital recorder) provides greater security.

Decide how consent will be recorded Consent processes can be less straightforward for online research; several methods are available, each with benefits and disadvantages. Asking participants to return a written consent form prior to data collection can place burdens on participants and requires a printer and scanner/smartphone. Online platforms (e.g. DocuSign) can be efficient but raise participant access, competency and data security concerns. Adobe Acrobat offers several methods including allowing participants to ‘sign’ via a smartphone screen, print and scan. Researchers can seek and record verbal consent (if acceptable to their ethics review board); this may be preferable, both for its lower burden on participants and to encourage the participant to ask questions before participating. Consider doing this in an introductory interaction (before the data collection event), especially for focus groups; this allows more attention to individual questions, and greater confidentiality. Flexibility is important as methods should suit participants’ comfort and capabilities.

Address online data collection challenges in ethics applications Ethics review boards will vary in their understanding of and tolerance for online data collection. As with face-to-face research, anticipate and address concerns: provide a logic for your study design, explain how the chosen data collection method(s) and platform meet the needs of the participants and the research topic. Be transparent about challenges and outline specific strategies for enhancing participation and offsetting risk. If your online research engages participants in new and unfamiliar locations, researchers should investigate whether their local ethics board approval will be sufficient to work in that context. Seeking advice from ethics review boards in advance can reveal common concerns and offer solutions.

3.2 Adapting to Technological Challenges: Hardware and Software

Planning ahead As online research events rely on the functionality and management of technology, both hardware and software, technological logistics should be central to research planning. Before commencing data collection, researchers should ensure that prospective participants have (1) access to hardware (e.g. phone, tablet, computer); (2) a reliable internet connection; (3) familiarity with the chosen platform; and (4) adequate support to respond to technological problems. Participants may need technical coaching and support before data collection occurs.

Affordances that facilitate desired social interactions Different online communication platforms have different affordances [ 4 ], and these functionalities enable, for example, different degrees of interactivity, data recording, confidentiality and privacy, and security (Box 1 ). Although ideally platforms would be chosen to suit the participants, in some instances a researcher’s institution, or local legislation will dictate the use of certain platforms for reasons including licensing or security. Issues to consider in selecting and managing the technological aspects of online research include the following.

Microphone and camera control: allows either, or both, participant or host to manually control their own or others’ cameras and microphones, helpful for managing background noise or speaking order if required.

Chat functions: allows short textual comments or questions to be posted by participants, usually in a sidebar from the main screen, and usually without disrupting the verbal conversation.

Breakout rooms: small subgroup discussions that can be separated out from the main meeting; host/s can join in and out, for example to answer or ask questions, or to facilitate discussions. Some platforms can automatically assign participants into rooms, with a mandatory timed finish, and automatically rejoin participants back into the main meeting.

Participant polling: short surveys or votes to gauge participant sentiments or show preferences.

Screen sharing: allows any participant to share the contents of their own screen, which is useful for sharing digital images or other materials the participant might want to introduce to the discussion.

Screen annotation: interactive screen-based textual and drawing tools, enabling participants to visually mark the content shown on screen.

Live subtitles and captioning: an additional service, often requiring subscription, that enables live subtitling of video calls, using a ‘speech to text’ recognition software. This may aid the participation of people living with hearing impairment [ 19 ].

Anonymity of participants If anonymity of participants is important, choose a platform that can easily control username displays and prepare participants to control how they present themselves. Some platforms display both first and surnames by default when entering an online meeting, therefore ensure participants know how to edit their display name. Avoid online platforms that require an account sign-up and automatically displays the user’s account name or contact phone number, as this compromises privacy and confidentiality. As participants may join the virtual research from their own homes or private offices, pre-research coaching should include the option of using virtual backgrounds for greater privacy protection.

Recording, screenshots and transcription Certain platforms offer recording of online interactions and transcription of audio data. Be sure to check how and where these data files will be stored and secured (see ‘Adapting to Ethical Challenges’ section). A screenshot allows anyone accessing the online event to take a photograph of the screen. This can be a useful tool in research but also allows participants to take recordings and screenshots without the knowledge of researchers and others. Consent for recording should be discussed with everyone taking part prior to commencing any online data collection activities, recording turned off for participants, and participants instructed not to make their own offline recordings.

Manually controlled or password entry Controlled entry by the host usually comes in the form of a ‘waiting room’, whereby the host manually admits participants. This gives hosts a greater degree of control but will also require more time and attention, particularly for larger groups. Password entry allows anyone with a password to the meeting to enter automatically and may save more time. Many research institutions and Human Research Ethics Committees (HRECs) already require password protection for online research.

Box 1 Platform functions checklist

When choosing an appropriate platform, check these specific technological affordances against the needs and suitability for your research method and participants:

For managing privacy, confidentiality and security of the participants and the research space:

✓ Password entry

✓ Admission and removal of participants

✓ Username display control

✓ Virtual background

For facilitating effective social interactions online

✓ Microphone and camera control

✓ Chat functions

✓ Breakout rooms

✓ Participant polling

✓ Screen sharing

✓ Screen annotation

For managing data collection and storage

✓ Built-in video and audio recording

✓ Subtitles and captioning

✓ Secure storage of recorded data

✓ Screenshot

3.3 Adapting to Social Difference: Knowing the Virtual Social Environment and Working with It

Compared with face-to-face research settings, researchers will have less control over potential interruptions to online data collection activities, as they cannot be physically present to offer alternative arrangements or interventions. Some participants may be practiced in online interactions as part of their daily work or social routine, while others will not [ 23 ]. Being prepared to manage interruptions, unpredictability and diversity of comfort level with online interactions is crucial. Below we suggest some adaptations to manage the social dimensions of online research.

Pre-research briefing/check-in Conducting a pre-research briefing can help participants be informed about what to expect and ensure they are comfortable using the online technologies and platforms. If you are working with participants who are vulnerable, have challenges in communicating, or are not familiar with using online technologies, supporting their communication and technology-use needs before data collection is crucial. This can also help build rapport to enhance participants’ relational satisfaction with participation.

Determining numbers in a focus group Compared with face-to-face research, online group interactions demand more cognitive effort for both moderators and participants [ 23 ]. Online interactions can also have a slower flow due to minor lags in screen interactions, which tends to exacerbate as the number of participants increase. Maximum numbers will likely be smaller than in face-to-face interactions; we recommend four to six participants for online focus groups. The goal is to not only ensure enough ‘energy’ in the room to sustain interaction but to also make facilitation manageable and the experience more enjoyable for participants.

Manage the energy in the ‘room’ Online focus groups and interviews require more than facilitating the content and flow of the discussion. Focused social interactions between people on a research topic, particularly with unfamiliar others, are particularly mentally demanding. Ways to manage this include slowing down the speed of the conversation with slightly longer pauses between sentences or questions and taking shorter breaks more frequently if a focus group runs for more than an hour. In our experience, online group modalities can encourage participants to take discrete turns rather than interacting in a dynamic flow; this may be offset to some extent by smaller group size and less intrusive moderation that creates more space for participant talk.

Use assistant moderators and make them co-hosts of the online call Assistants can help manage the technology while remaining muted with the camera off in the background. This can reduce cognitive burden for the moderator, allowing greater focus on the conversation. Ensure the assistant moderator role is explained to participants at the start of research events.

Designate personnel for emotional support In addition to an assistant moderator, a ‘runner’ or research assistant can act as a point of support for participants in difficulty. The role of this person should also be explained to all participants. Some participants may also wish to access support more discretely, and how this can be done should also be made clear.

Establish a culturally safe research space In any research, whether face-to-face or online, participants should feel culturally safe [ 24 ]. Managing the cultural safety of online interactions, particularly in group research, may sometimes be more challenging because visual cues that threaten cultural safety may be more difficult to read and respond to. Moderators need to establish ground rules early to set the tone and expectations of the room and be firm and decisive in using microphone control to temporarily mute disrespectful participants, or, in unresolvable situations, have an assistant remove them. Check that the selected technological platforms will allow the host to eject or temporarily mute a participant if necessary. Assistant moderators can also keep track of chat room interactions to help manage any challenging circumstances. While some online platforms (e.g. Zoom) can facilitate the provision of language interpretation via simultaneous audio channels, we note that ensuring cultural safety requires more than interpretation, and that adding additional channels does add technological and interpersonal complexity.

Manage microphones and background noise While asking participants to mute their microphones can often minimise background noise, having to turn the microphone on and off during interactions will also interrupt the flow of interactions. To maximise participation, leaving microphones on is recommended, despite the trade-off with background noise, which can interfere with data quality and the experience of other participants. Asking participants to do their best in minimising background noise or asking an assistant moderator to mute individual participants if background noise becomes problematic may help manage this. Discuss the preferred arrangement with participants at the start of the research event, including when and if microphones should be muted, and the best way to manage when to speak.

Have a back-up plan Sometimes technology can go wrong (computers crash, hardware malfunctions, internet connections go down), either halting the research or producing inaudible content. We have already considered the need for a clear, agreed backup plan to manage distress and cultural safety; this is also important to manage technical problems. Assistant moderators should hold a list of participants’ contact phone numbers and clear agreement with participants on when their contact number will be used. Moderators should be decisive about when to abandon the online platform and move to the back-up plan.

Manage unexpected intrusions ‘Zoom bombers’ join online meetings uninvited. They can cause interruption and embarrassment and they breach the privacy of a confidential research event. ‘Zoom bombing’ happens mostly when a link to the meeting is posted publicly and becomes searchable online. Use a private password for every online research event and consider using a waiting room for more control. Explicitly ask participants not to post events publicly or share links, and ensure passwords are secure and not publicised (e.g. on social media).

Conduct evaluation, and research online qualitative research Consider including questions about the use of the technology and online platform in post-research evaluations; feedback can not only be used to refine design and processes in future research but can also support methodological research.

4 Conclusions

Online methods were once marginal in qualitative research, rarely considered a first choice for data collection, and restricted mostly to those researchers who were interested in online worlds such as social media or gaming cultures as a subject of study. This has radically shifted. At the time of writing, the COVID-19 pandemic has driven much of everyday life into virtual worlds, as families, workplaces and existing social networks try to sustain themselves in the face of the risk of transmission. Niels Bohr allegedly quipped that prediction is very difficult, especially about the future; allowing for this caveat, we cannot imagine a future where everyday life or research practices return exactly to a 2019 pre-pandemic status quo. Online qualitative research has opened up a world of options for accessing participants and creating new types of data, and this seems likely to continue to expand. Qualitative researchers, then, need to respond to these new circumstances and opportunities in methodologically and ethically sound ways.

This paper is limited by our knowledge, experience and reading. Others will have expertise that we do not (e.g. in assistive communication technologies). We are also writing in a particular moment—a pandemic-induced flight to online research. As online qualitative research becomes mainstream, it is likely that technologies, practices and understandings will mature. Because change is inevitable, we have focused on principles rather than fine details of different platforms. There may be scope for researchers to engage with platforms over time and demand technological innovations that will more easily serve the ethical and methodological needs of research practice. Researchers themselves will also generate new qualitative methods that respond to the particularities of online platforms and their affordances. If researchers remain focused on design principles and attend to ethical, technical and social challenges, online methods will continue to make a significant contribution to qualitative health preferences research.

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Acknowledgements

The authors would like to thank Dr. Bridget Haire for permission to use the interview excerpt, and Lucy Carolan for assistance with the submission process.

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." [2] Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Qualitative Research Approaches

Ethnography

Ethnography as a research design originates in social and cultural anthropology and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc, through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded theory

Grounded Theory is the "generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior." [5] Unlike quantitative research, which is deductive and tests or verifies an existing theory, grounded theory research is inductive and, therefore, lends itself to research aimed at social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is the "study of the meaning of phenomena or the study of the particular.” [5] At first glance, it might seem that Grounded Theory and Phenomenology are pretty similar, but the differences can be seen upon careful examination. At its core, phenomenology looks to investigate experiences from the individual's perspective. [2] Phenomenology is essentially looking into the "lived experiences" of the participants and aims to examine how and why participants behaved a certain way from their perspective. Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources. In contrast, Phenomenology focuses on describing and explaining an event or phenomenon from the perspective of those who have experienced it.

Narrative research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called a "thick" or "rich" description and is a strength of qualitative research. Narrative research is rife with the possibilities of "thick" description as this approach weaves together a sequence of events, usually from just one or two individuals, hoping to create a cohesive story or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be "opportunities for innovation." [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. [4] It is valuable for qualitative and quantitative researchers to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontologies and epistemologies. Ontology is defined as the "assumptions about the nature of reality,” whereas epistemology is defined as the "assumptions about the nature of knowledge" that inform researchers' work. [2] It is essential to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a complete understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, researchers must understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist versus postpositivist

To further understand qualitative research, we must discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social and natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in their research. It stems from positivist ontology, that there is an objective reality that exists that is wholly independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained, but could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world,” and therefore, postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are also constructivist, meaning they think there is no objective external reality that exists but instead that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. "Constructivism contends that individuals' views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality.” [6]  constructivist thought focuses on how "reality" is not a fixed certainty and how experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike positivist views, that there is not necessarily an "objective"reality we all experience. This is the ‘relativist’ ontological view that reality and our world are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have. It can even change the role of the researchers. [2] For example, is the researcher an "objective" observer, such as in positivist quantitative work? Or is the researcher an active participant in the research, as in postpositivist qualitative work? Understanding the philosophical base of the study undertaken allows researchers to fully understand the implications of their work and their role within the research and reflect on their positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale for being the most informative.
• Criterion sampling selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques, including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic, and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant-observers to share the experiences of the subject or non-participants or detached observers.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed, which may then be coded manually or using computer-assisted qualitative data analysis software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results could also be in the form of themes and theory or model development.

Dissemination

The healthcare team can use two reporting standards to standardize and facilitate the dissemination of qualitative research outcomes. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research. [13]

Applications

Many times, a research question will start with qualitative research. The qualitative research will help generate the research hypothesis, which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data to better understand what the numbers truly mean and their implications. The qualitative techniques can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research, researchers can explore poorly studied subjects with quantitative methods. These include opinions, individual actions, and social science research.

An excellent qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure no omissions of part of the target population. A proper collection method should be selected that will help obtain the desired information without overly limiting the collected data because, often, the information sought is not well categorized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of why teens start to smoke and factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered "cool," and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current nonsmokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the survey results to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the primary factor that keeps teens from starting to smoke, and peer pressure was the primary factor that contributed to teens starting smoking. The researcher can go back to qualitative research methods to dive deeper into these for more information. The researcher wants to focus on keeping teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly in the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure to smoke. The researcher finds a local park where many local teenagers hang out and sees that the smokers tend to hang out in a shady, overgrown area of the park. The researcher notes that smoking teenagers buy their cigarettes from a local convenience store adjacent to the park, where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk populations their perceptions of the changes and what factors are still at play, and quantitative research that includes teen smoking rates in the community and the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or combined with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation not only to help generate hypotheses that can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are. Qualitative research allows researchers to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many ways, including the evaluation criteria. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. Credibility, transferability, dependability, and confirmability are the correlating concepts in qualitative research. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept on the left and the qualitative concept on the right:

• Internal validity: Credibility
• External validity: Transferability
• Reliability: Dependability
• Objectivity: Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid, qualitative researchers should ensure that their work has credibility. 

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple data collection methods to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable if we interviewed the magician, backstage hand, and the person who "vanished." In qualitative research, triangulation can include telephone surveys, in-person surveys, focus groups, and interviews and surveying an adequate cross-section of the target demographic.
• Peer examination: A peer can review results to ensure the data is consistent with the findings.

A "thick" or "rich" description can be used to evaluate the transferability of qualitative research, whereas an indicator such as an audit trail might help evaluate the dependability and confirmability.

• Thick or rich description:  This is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was conducted. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original information records should also be kept (eg, surveys, notes, recordings).

One issue of concern that qualitative researchers should consider is observation bias. Here are a few examples:

• Hawthorne effect: The effect is the change in participant behavior when they know they are being observed. Suppose a researcher wanted to identify factors that contribute to employee theft and tell the employees they will watch them to see what factors affect employee theft. In that case, one would suspect employee behavior would change when they know they are being protected.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens unconsciously for the participant, so it is essential to eliminate or limit the transmission of the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in contrived scenarios with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative or quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research is not an island apart from quantitative research but an integral part of research methods to understand the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is essential for all healthcare team members as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research. Much of the qualitative research data acquisition is completed by numerous team members, including social workers, scientists, nurses, etc. Within each area of the medical field, there is copious ongoing qualitative research, including physician-patient interactions, nursing-patient interactions, patient-environment interactions, healthcare team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Research Article

Implementation of the My Abilities First Tool: A qualitative study on the perceptions of professionals, caregivers, children, and adolescents with disabilities

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Physiotherapy, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil

Roles Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Physiotherapy, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

Affiliation Division of Medical Sciences, University of Victoria, Victoria, BC, Canada

Roles Data curation, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Postgraduate Program in Decision Models and Health, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

Roles Data curation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Faculty of Physiotherapy, Hemi Child-Research Unit, University of Castilla-La Mancha, Spain

Roles Conceptualization, Data curation, Methodology, Visualization, Writing – review & editing

Affiliation Postgraduate Program in Public Health Collective Health Center Health Sciences Center, Rio Grande do Norte, Brazil

Roles Data curation, Formal analysis, Visualization, Writing – review & editing

Affiliation Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Postgraduate Program in Neuroengineering, Macaiba, Brazil

Roles Data curation, Visualization, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review & editing

• Roselene F. Alencar, 
• Egmar Longo, 
• Verónica Schiariti, 
• Caline C. A. F. Jesus, 
• Rocío P. Carrión, 
• Carolina D. L. Alvarez, 
• Monique L. G. Coelho, 
• Tatiana F. C. Pereira, 
• Maria C. L. Cruz, 

• Published: May 20, 2024
• https://doi.org/10.1371/journal.pone.0301718
• Peer Review
• Reader Comments

To analyze the perceptions of professionals, caregivers, children, and adolescents with disabilities regarding the implementation of the My Abilities First (MAF) tool in Specialized Child Rehabilitation Centers (CERs).

This is a qualitative research based on Reflexive Thematic Analysis (RTA). The study involved twenty-seven intentionally selected individuals, comprising 12 physiotherapists, 4 occupational therapists, 11 caregivers, 9 children and 2 adolescents. Participants completed sociodemographic and clinical questionnaires and took part in semi-structured online interviews, focusing on two themes: Positive health approaches and the MAF tool. The study was approved by the local ethics committee (opinion 4.779.175).

Reflexive Thematic Analysis of the interviews resulted in two themes: (1) Perceptions regarding the MAF tool as an educational and contributory process to enhance the inclusion and participation of children and adolescents with disabilities, and (2) Barriers and facilitators for the implementation process of the MAF tool. The implementation of MAF was identified as a driving factor in promoting equity and increased participation of children and adolescents with disabilities in various settings, including health, education, and leisure. Interviewees highlighted the need to confront attitudinal, communication, and social barriers that may hinder the implementation of the tool.

The implementation of the MAF tool was perceived as an innovation due to its focus on the abilities of individuals with disabilities. However, there is a need to restructure it to broaden its scope and access to different contexts in order to confront barriers and enhance the inclusion and participation of children and adolescents with disabilities.

Citation: Alencar RF, Longo E, Schiariti V, Jesus CCAF, Carrión RP, Alvarez CDL, et al. (2024) Implementation of the My Abilities First Tool: A qualitative study on the perceptions of professionals, caregivers, children, and adolescents with disabilities. PLoS ONE 19(5): e0301718. https://doi.org/10.1371/journal.pone.0301718

Editor: Renato S. Melo, UFPE: Universidade Federal de Pernambuco, BRAZIL

Received: December 16, 2023; Accepted: March 21, 2024; Published: May 20, 2024

Copyright: © 2024 Alencar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In 2020, as the coronavirus (COVID-19) pandemic spread across the world, public health strategies, including social distancing measures, were implemented, causing disruptions in people’s routines [ 1 – 5 ]. For children and adolescents with disabilities and their families, such measures interfered with access to healthcare, which is essentially provided through rehabilitation services. These measures brought visibility to and exacerbated existing difficulties, sparking discussions and demanding innovative actions, inclusive approaches, and those grounded in ensuring the rights of persons with disabilities [ 2 , 6 , 7 ].

Given this context, Schiariti and McWilliam (2021) [ 2 ] suggested measures for attention to persons with disabilities that can be implemented virtually, using low-cost online platforms suitable for children: (1) establishing collaborative and meaningful goals, (2) focusing on abilities, (3) empowering families for healthcare, (4) emphasizing the right of children and adolescents to express their feelings and opinions, (5) promoting emotional connections between professionals and families, (6) changing attitudes towards disability, (7) improving the provision of remote services, and (8) applying early intervention beyond the age of five [ 2 ].

In this regard, the educational tool My Abilities First (MAF), conceived by Verònica Schiariti and disseminated in 2020, aims to achieve these measures of attention to people with disabilities. MAF is grounded in the use of positive language and the process of informing professionals, family members, and the general public about the importance of positive attitudes towards people with disabilities [ 6 ]. According to Schiariti (2020a), the use of positive language, associated with the MAF approach, means paying attention to what the person with a disability can do. MAF proposes the adoption of an abilities identification card, the My Abilities ID CARD [ 2 , 6 ]. Furthermore, it is a proposal that, by valuing the abilities of children and adolescents with disabilities, can contribute to increased community engagement and effective participation [ 6 , 8 – 10 ].

Historically, approaches directed towards people with disabilities have neglected functional aspects, contextual influences, and the importance of family in the process [ 6 , 7 ]. These biases become more evident when one observes that the functional abilities of individuals with disabilities are often assessed only by professionals. Additionally, there is frequently limited collaboration between professionals and families, hindering the attainment of a more comprehensive and accurate view of the abilities of children and adolescents with disabilities in a wider range of situations and environments [ 8 – 12 ].

Furthermore, family-centered interventions are globally recommended, yet the focus on the individual and the clinic still prevails [ 6 , 9 ]. In recent years, studies indicate an increase in families seeking to express their needs for involvement in healthcare decision-making. Studies highlight this importance, as well as involving these families in information sharing [ 12 – 16 ]. In a study by Bamm and Rosenbaum (2008) [ 17 ], families considered access to information as paramount, while professionals deemed providing education essential to better promote family-centered care, thus offering better quality services for the health of children and youth with disabilities [ 17 ].

Therefore, considering the guidelines of the Convention on the Rights of Persons with Disabilities in addressing these issues, it becomes evident that promoting positive health actions plays a fundamental role in stimulating innovations and empowering people with disabilities [ 18 , 19 ]. This emphasizes the importance of ensuring that individuals with disabilities receive attention widely, equally, and equitably compared to the rest of the population [ 18 – 21 ].

Given the above, since 2021, our research group has initiated a collaboration to implement the MAF tool for the first time in Brazil. Therefore, the study’s objective was to analyze the participants’ perceptions regarding the implementation of the MAF tool in Child Rehabilitation Centers (CERs). We believe that exposing the participants’ experiences and interpretations regarding these topics can generate reflections and future actions that surpass our initial purposes. It goes beyond the attention on physical structure and body function but focuses on achieving better levels of activity and participation among children and adolescents with disabilities in different contexts.

Materials and method

Study design.

This study follows a qualitative, descriptive, reflexive, and interpretative approach. The choice of Reflexive Thematic Analysis (RTA) aligns with the qualitative principles of this study, particularly considering the need to analyze responses from semi-structured interviews. Additionally, RTA was selected for emphasizing the importance of active participation and the researchers’ theoretical grounding by immersing themselves in the data to address the research questions [ 22 , 23 ].

Study setup

The data was collected from three distinct groups of participants (professionals, caregivers, and children/adolescents) who attended Specialized Child Rehabilitation Centers (CERs) located in three municipalities in the state of Rio Grande do Norte (RN), in the northeastern region of Brazil: Macaíba, Parnamirim, and Natal.

Ethical aspects

The project was approved by the research ethics committee of the Faculty of Health Sciences of Trairi (Federal University of Rio Grande do Norte/Brazil—CAAE: 43972321.1.0000.5568 and opinion 4.779.175). Volunteers signed the consent form for images and audio authorization and the Informed Consent Form (ICF), following Resolution No. 466/2012 of the National Health Council/MS, which regulates research involving human subjects.

Study phases and period

The study was organized into 5 main stages. After signing the ICF, participants completed a sociodemographic and clinical questionnaire. Subsequently, they took part in semi-structured interviews and workshops. These activities occurred from September 2022 to March 2023 ( Fig 1 ).

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Source: Research Data.

https://doi.org/10.1371/journal.pone.0301718.g001

Participants and eligibility criteria

Participant recruitment was intentionally conducted [ 24 – 26 ]. Selection took +place through active search in the Child Rehabilitation Centers (CERs) by the research team members, utilizing direct personal contact, phone calls, and messaging apps. The mentioned rehabilitation centers accommodated approximately 90 individuals with the potential to participate in the study. All of them were contacted; however, 27 participants met the inclusion criteria and responded to the semi-structured questionnaires and interviews I and II, after signing consent forms and authorization for images and audios.

The following inclusion criteria were considered for participants: (1) be a physiotherapist or occupational therapist involved in pediatric healthcare; (2) be a child or adolescent with disabilities, aged between 6 and 14, of all genders, and accompanied by a caregiver with some degree of kinship; (3) have an active registration in one of the selected CERs for the study (for professionals, children/adolescents); (4) understand and accepting the study proposal; and (5) be capable of accessing basic communication resources through electronic means to participate in planning, workshops, and to respond to online data collection instruments.

Exclusion criteria included: (1) failure to respond to the sociodemographic questionnaire or semi-structured Interview I; (2) disengagement from the Child Rehabilitation Center (CER) to which the participant would be enrolled during the study period.

Twenty-seven participants were included and recruited to respond to semi-structured Interviews I and II. However, only 15 participants responded to semi-structured Interview II. Further details can be found in Fig 2 .

https://doi.org/10.1371/journal.pone.0301718.g002

Data collection instruments

For data collection, the following were used: (1) Sociodemographic and clinical questionnaires and (2) Semi-structured interviews (I and II).

Sociodemographic and clinical questionnaire.

The researchers developed questionnaires based on the study’s themes and existing literature [ 6 , 27 , 28 ]. The questionnaire administered to professionals aimed to gather information on: (1) personal identification, (2) professional category, and (3) professional experience history, clientele served, and interventions used with children/adolescents with disabilities ( https://forms.gle/NSRfVxjRRcdUZ9nT6 ). The questionnaire directed towards children/adolescents and their caregivers aimed to collect information on: (1) personal identification, (2) socioeconomic and educational conditions, and (3) understanding of the child/adolescent’s overall abilities ( https://forms.gle/9axVTXcCBUhxhqWA7 ).

Semi-structured Interview I and II.

Semi-structured Interviews I and II were administered to all participant groups. In semi-structured Interview I, conducted with caregivers and children/adolescents with disabilities, the objective was to explore the dialectical relationship between positive health attitudes and topics related to valuing the abilities of children and adolescents with disabilities ( https://forms.gle/qN2k9pZc5JMSfAfv8 ). However, in semi-structured Interview I applied to professionals, apart from this objective, the aim was to understand the feasibility of implementing MAF ( https://forms.gle/iieRJEKNmVJQfjia8 ).

During semi-structured Interview II, information related to the perceptions of professionals ( https://forms.gle/PU34GdRSmJ5gb8xW6 ) and families ( https://forms.gle/oL7BBqCSeUebE24J6 ) about the implementation process of the MAF tool was sought. At this stage, open-ended questions were formulated by the researchers based on the study’s themes [ 2 , 6 , 7 ].

Alphanumeric codes were assigned to the statements obtained from semi-structured Interview II to preserve the interviewees’ identities. ’C’ was used to represent family members (Caregivers, Children, and Adolescents), ’F’ for physiotherapists, and ’OT’ for occupational therapists, followed by numbers indicating the interview sequence in the system.

Time taken to respond to semi-structured Interview I ranged from 10 to 20 minutes, whereas for Interview II, it ranged from 20 to 40 minutes. All interviews were digitally recorded for literal transcription and data analysis.

My Abilities First Tool

The MAF tool incorporates guiding principles for services aimed at children and adolescents with disabilities and their families. These principles encompass the valuing of capacity and abilities and the potential to achieve goals. Additionally, these involve the interactions between the individual and the environment, promote family-centered care, and adopt a biopsychosocial rights-based approach for assessments, planning, and interventions. Ultimately, they aim to empower both families and individuals with disabilities to participate in decisions related to their own care [ 2 , 6 ].

The MAF tool offers animations (videos) with objectives centered on people with disabilities: (1) The first video introduces the application of an abilities-oriented approach in healthcare encounters. It proposes the creation of an abilities identification card, the My Abilities ID Card. Target audience: healthcare professionals and related fields ( https://youtu.be/WyW6ey3kHvM ); (2) The second video highlights the importance of applying a holistic approach in routine health consultations through questions to identify abilities. Target audience: healthcare professionals, researchers, educators, and students ( https://youtu.be/Dnn_-0IEe_Q ); and (3) The third video promotes a change in attitude towards disability and individuals with disabilities. In this animation, a typically developing child advocates for the inclusion of a child with a disability. The target audience is the general public, primarily school-aged children with disabilities and their peers ( https://youtu.be/myHFKggNeGc ) [ 2 , 6 ].

Educational interventions

Two workshops were conducted to share knowledge and facilitate active participation in the study, particularly in Semi-Structured Interview II.

The theoretical workshop aimed to present, discuss, and provide theoretical content on the My Abilities First theme and its implementation process in CERs. Scientific articles related to the study’s theme and animations inherent to the MAF tool (mentioned in My Abilities First Tool section) were utilized and shared among researchers and participants.

The practical workshop, on the other hand, provided practical content with the following objectives: to create the Abilities Identification Card, the My Abilities ID Card, and to adopt strategies for using this card and disseminating the animations (professionals: during appointments; caregivers and children/adolescents: at home, school, within the community, and in healthcare services). For further information, please refer to Table 1 .

https://doi.org/10.1371/journal.pone.0301718.t001

Data analysis

The quantifiable data from the sociodemographic/clinical questionnaires and Semi-Structured Interview I were analyzed and presented by calculating the percentages in relation to the total observations in the dataset. Non-quantifiable data (verbal responses) were transcribed in full for presentation in figures. Further information can be found in the results section of this study.

The analysis of semi-structured interviews II was based on Reflective AT and was conducted by the main researcher (RFA) and coordinating researcher (ARRL), assisted by an experienced collaborator (CCAFJ) [ 23 , 24 ]. For better interpretation of the data and preparation of the report, the process was outlined according to the six stages of thematic analysis by Braun and Clarke (2006;2013;2019) [ 22 , 25 , 26 ], presented in Table 2 .

https://doi.org/10.1371/journal.pone.0301718.t002

The entire process, including the six stages (as in Table 2 ), will be described below and is documented and archived for clarity and traceability, consequently ensuring greater rigor and reliability of the research [ 28 – 30 ].

Initially, in the familiarization stage, the interviewees’ speeches were transcribed in full by three volunteers (TFCP, SSVM and MCLC), checked by the researchers (RFA, ARRL and CCAFJ), and the interview participants themselves ( Table 2 ). Subsequently, the transcribed speeches were organized into a Word document. Readings and rereadings of all the material continued throughout the entire process, but primarily at this stage.

In the subsequent stages, the coding process and the construction of themes predominantly followed an inductive approach, aiming to remain faithful to the participants’ responses. Nevertheless, a certain degree of deductive analysis was employed, emphasizing the importance of recording the subjectivity of less apparent factors in the responses [ 28 – 30 ].

The themes were identified through predominantly semantic coding, meaning that the description of the speeches did not require continuous exploration of hidden meanings, due to the clarity of these speeches and the focus on a specific theme, which was the implementation process of the MAF tool. However, there was some latent level coding when it was necessary to employ a more interpretative exercise of the speeches, considering, to justify this need, the participants’ premises, context, and social position, such as speeches with the association of regional terms in unfinished sentences.

The analysis was dynamic and required, following Braun and Clarke (2020) [ 24 ], observations, records, revisions, exclusions, and new inclusions of content, that is, it required moving forward and/or returning [ 24 ]. However, the basis of the process was sequenced throughout the thematic analysis [ 23 – 25 ].

For the recording and construction of each theme, the process involved the study’s literary foundation, the expertise of the researchers and collaborators (RFA, ARRL and EL) to generate codes, gathering relevant excerpts, grouping them into potential themes and sub-themes, and thus reaching the stage of definitively naming the themes ( Fig 3 ). With this refinement, in line with Braun and Clarke (2019) [ 22 ], to achieve the final map of the data analysis process, 11 codes encompassed the breadth of ideas and supported the definition of themes and the report writing, contributing to answering the research question and thus meeting the overall study objective. As a result, two themes emerged: Theme 1 - ’Perceptions of the MAF tool as an educational and contributory process to enhance the inclusion and participation of children and adolescents with disabilities,’ and Theme 2 - ’Barriers and facilitators for the implementation process of MAF’ ( Fig 3 ).

Source: Research data.

https://doi.org/10.1371/journal.pone.0301718.g003

The study involved 12 physiotherapists, 4 occupational therapists, 11 caregivers, 7 children, and 2 adolescents. The majority were professionals with 11 or more years of experience in pediatric care (66.7%), physiotherapists (73.3%), and within the age range of 30 to 49 years old (81.4%). Only 33.3% reported participating in activities related to positive language in health, a foundational aspect for the MAF tool. Further details are presented in Fig 4 .

https://doi.org/10.1371/journal.pone.0301718.g004

A total of 11 children/adolescents with their caregivers participated in the study. Diverse sociodemographic and clinical characteristics were recorded, and percentages were calculated based on the numerical values assigned to each question, aiming for better exposure and transparency of the obtained data. The predominant age of the children and adolescents was 7 years (54.5%), and all attended regular education. Further sociodemographic information of this group is available in Table 3 .

https://doi.org/10.1371/journal.pone.0301718.t003

In this questionnaire, children, adolescents, and their caregivers answered questions about their abilities. There was a greater appreciation of the abilities of children and adolescents within the home (50% always and 20% often) compared to outside it (20% always and 10% often). When asked about what should happen to value these abilities and about the abilities they would like to develop, there was a variety of responses based on the specific needs of these participants. The responses are presented in Fig 5 .

https://doi.org/10.1371/journal.pone.0301718.g005

Regarding the responses from semi-structured interview I, it was identified that although 60% of professionals claimed to be familiar with positive language and attitudes in health and considered it feasible to apply in their daily practices, just over half of them (36.4% always and 18.2% often) address the abilities of interest of the child/adolescent in their treatment programs. Further information is presented in Fig 6 .

https://doi.org/10.1371/journal.pone.0301718.g006

The children/adolescents and caregivers were unanimous in emphasizing the importance of valuing abilities. For 90% of the families, professionals encourage children/adolescents with disabilities to acknowledge their own abilities, and more than 50% of the families report that professionals value the abilities of interest of the children/adolescents ( Fig 7 ).

https://doi.org/10.1371/journal.pone.0301718.g007

The analysis of Semi-structured Interview II was based on Reflexive Thematic Analysis and resulted in the construction of two themes grounded in 15 interviews (10 professionals and 5 families), namely: (1) Perceptions about the MAF tool as an educational and contributory process to enhance the inclusion and participation of children and adolescents with disabilities, and (2) Barriers and facilitators for the implementation process of MAF.

The first theme was constructed from responses regarding the knowledge of the MAF tool and its usefulness. Interviewees demonstrated an understanding of the tool’s proposal and considered it applicable. Concerning professionals, the majority agreed that the MAF tool could be useful in healthcare for children and adolescents, highlighting the positive impact of the possibility to change the dynamics between interventions that focus on disability and those that value abilities. Furthermore, they pointed out the MAF tool as a means to promote changes in professional and family behaviors regarding the perception of abilities.

"Yes. […] To stop focusing only on limitations, illness, or disability and see the potentialities […]. Specifically regarding physiotherapy, this tool can serve as a guide […]. Besides, I think it can also help families to have another vision about disability […] families cling to unrealistic expectations […] It is necessary to respect the child as a being who has desires […] not to deprive… and not necessarily meet the expectations of parents or society… in a certain pattern." (F1) "Yes, I think it’s very useful to change the perspective of therapists and families and consequently of children and adolescents about disability and the individual." (TO2)

However, the participants expressed concerns regarding the credibility of the feasibility of the proposal, which requires behavioral and cultural changes that often depend on public policies. This uncertainty about the implementation and usefulness of the tool was evident in the statements of the families, such as C1 and C2.

"I think so. But, there needs to be a way to use it." (C1) "Yes, but it needs to go beyond just being theoretical." (C2)

The participation of the child or adolescent with the caregiver in the same space with the professionals was considered important for the implementation process of the MAF tool, according to these families, regardless of the category (families or professionals). They further added that the tool itself provides this educational and inclusive opportunity, involving the acquisition and sharing of knowledge.

"Yes, it’s useful, very good to be able to talk about the abilities that a child with a disability has, and to be able to do it in the way that they know." (C3) "Yes, and because we are being listened to more, it’s good to know that my opinion matters."(C4)

In general, participants highlighted positive aspects related to the MAF tool. However, concerns about the potential for increased discrimination, ableism, among others, were expressed in statements throughout the study, particularly in the statement of C5 below, which illustrates this context and has the potential to stimulate further reflections:

"No! I think it’s unnecessary to invest in the idea of highlighting the abilities of the person with a disability in the first place, because it can further highlight the disability."(C5)

Interviewees showed, at times, a certain disinterest and difficulty in expressing opinions due to insufficient knowledge about the tool. However, when analyzing strategies to improve the tool, professionals emphasized the pressing need for an evidence-based educational process and behavioral changes.

"Yes. Once the potentialities are visible, it diminishes the preconceived notions that disabilities only accompany difficulties. It opens up the perception that there are also facilities, and through them, responses, interaction, and understanding can be established to set goals in caring for children and adolescents with disabilities.” (F2) “Yes. From knowing the abilities of children with disabilities, we, as therapists, can work to further enhance such abilities.” (F4) “Yes, as by initiating the treatment and enhancing the abilities, the patient x therapist bond is established in less time, the family learns to value the present abilities from the beginning and how to stimulate them, and finally, we put the patient on stage and active from the start of the sessions.” (TO3)

For the interviewees, the enhancement of the MAF implementation process requires dissemination, use in various contexts, and for a wide audience. From the perspective of these interviewees, restricting access to the tool also means limiting access to knowledge and the processes of inclusion of people with disabilities.

Using it everywhere and not just in healthcare settings." (C1) "Explaining it to people everywhere." (C2) "Taking it to schools, public transportation stops, neighborhoods." (C3) "It works if used for those with disabilities and also for those without disabilities." (C5)

The school was mentioned as the appropriate place for the dissemination and use of the tool due to the daily interaction with people outside the family circle. Among professionals and families, similar responses were observed regarding what is necessary for the implementation process of MAF. Access to information, extensive dissemination, and usage in different contexts were emphasized.

"I believe that thinking of ways to make it something that encompasses everyone and not just people with disabilities could be positive. It would be a way to put everyone’s abilities into perspective, without necessarily being a tool used only by people with disabilities." (F2) "By spreading it to the largest number of professionals in the field, families, and including it in assessment protocols and anamnesis." (TO2)

Regarding the responses, which formed the basis for the construction of the second theme: ’Barriers and facilitators for the implementation process of MAF,’ it was observed what is possible for the mentioned process to succeed.

Families were assertive in highlighting the need for resolution-oriented actions for usage. However, participants emphasized the need to avoid using the MAF tool to highlight individuals with disabilities and thereby generate discrimination and other stigmas. Additionally, participants suggested the importance of ensuring training for the proper use of the tool.

“Using it to highlight the child, discriminate." (C1) "Staying on paper." (C2) "Using more rigor, more ’authority’ to make the patient trust, to make the patient feel secure." (C5) "[…] if the therapist is not confident about the tool, there may be some bias." (F7)

When asked about what could go wrong in using the MAF tool, participants highlighted the need to address barriers related to disability and the life of a person with disabilities, such as attitudinal, communication/information, political, and social barriers.

"The stigma of being." (TO1) "I don’t see how it could go wrong if well indicated." (F1) "I am concerned that the activities listed in My Abilities First might be perceived as what the person can do and that this ends up ’defining’ who they are and what they can or cannot do. It needs to be clear that these are ways to interact and demonstrate what one likes, what one does, but that it does not limit the person to what they can do." (F2) "In my opinion, nothing can go wrong, as long as it is applied with common sense, caution, and according to reality." (TO2) "Difficulty for children to express what they like to do." (F3) "The family is not adhering." (F5)

When asked to identify reasons to recommend the adoption of the MAF tool, families shared arguments related to the purposes of the tool:

"s will be recognized." (C1) "See abilities before the disability." (C2) "One reason to recommend is that yes, it is possible for a child with a disability to do it their own way." (C3) "I recommend it because it helps people with disabilities to give their opinion on how it can improve." (C4) "If it gives more security in the application, it will generate more results." (C5)

The reasons for recommending the tool, pointed out by the professionals, were accompanied by the desire to restructure the tool and expand objectives. However, the recommendations were to facilitate more humane interventions and promote equity, as well as enhance inclusion.

"Facilitation" (TO1) "The card with the abilities could be used when the child has difficulty communicating, which would help include them in activities at school, for example, respecting what they like and are able to do." (F1) "Facilitating the understanding that the universe of a person with disabilities is not just limited, as the tool ’my abilities first’ proposes, can assist these individuals in various areas of their lives, from meeting a new therapist to the school environment, through family, community, and the entire surroundings. Getting to know the child better through their abilities makes interaction and social inclusion easier." (F2) "Self-esteem, seeing individuals with disabilities as individuals of possibilities." (TO2) "Shifting the intervention focus from structure and function to activity and participation." (F3) "Valuing children and young people with disabilities and further enhancing their abilities." (F4) "Seeing the child as a person, not a pathology." (F5) "In my view, the primary purpose is to put the patient in the spotlight right from the first interaction, active and participative, as families sometimes fail to notice certain present abilities or what they can do in their own way." (TO3)

The respondents’ answers reflect a variety of perceptions about the implementation and usefulness of the MAF tool. While some highlighted potential benefits, such as increased self-esteem, appreciation of individual abilities, and contribution to promoting inclusion, others expressed concerns about possible adverse effects, such as increased discrimination and stigma. There was also a common emphasis on the importance of a person-centered approach, recognizing individual abilities and the importance of active participation of individuals with disabilities in their communities. These diverse perspectives offer valuable insights to guide future research and practices in the field of children and adolescents’ rehabilitation.

This study, which describes the first experience of implementing the MAF tool in Brazil, aimed to analyze the perceptions of physiotherapists, occupational therapists, children, adolescents with disabilities, and their caregivers regarding the implementation of the My Abilities First (MAF) tool in Pediatric Specialized Rehabilitation Centers (CERs).

The MAF is seen as an innovative tool because it emphasizes the individual abilities of people with disabilities, rather than focusing on their disabilities, and it is aligned with the United Nations Conventions on the Rights of the Child and on the Rights of Persons with Disabilities, embracing the rights of self-determination, autonomy, and dignity of people with disabilities [ 2 , 6 , 18 , 19 ].

The heterogeneity of the participants (age range, cultural and social aspects) impacted the meticulous development of the data collection instrument questions and applicability. However, the study provided an integrative environment in which perceptions about the MAF tool and its implementation were shared.

Regarding Theme 1: ’Perceptions of the MAF tool as an educational process and contribution to enhance the inclusion and participation of children and adolescents with disabilities,’ participants demonstrated understanding of the tool and considered it applicable in their contexts, whether in therapeutic planning, in the services where they work, or in their daily lives, as mentioned by Schiariti (2020) [ 6 ] when referring to the tool’s accessibility in different aspects.

Both participant groups emphasized the importance of educational workshops and ’listening’ to the children/adolescents and families in the study. These aspects allow for greater integration and participation not only of the child or adolescent but also of the family in the therapeutic context, as evidenced by Palisano et al. (2009) and Almasri et al. (2011) [ 14 , 15 ]. This approach facilitates the broad involvement of the family in the therapeutic process, significantly contributing to adherence to services, therapeutic planning, and consequently, the use of the proposed tool.

The inclusion of children/adolescents with disabilities and their families in the planning or any other stage of a therapeutic plan or even valuing their abilities, are positive aspects related to the MAF tool. These aspects favor the empowerment of this population and make them protagonists of their rehabilitation. These data are aligned with the discussions of Schiariti & McWilliam (2021), Schiariti (2020), and Palomo-Carrión et al. (2022) [ 2 , 6 , 10 ]. These authors, in mentioning collaborative and empowering approaches for children and adolescents with disabilities, emphasize the importance of focusing on abilities and respecting the rights of people with disabilities to express feelings, opinions, and their preferences.

Another point highlighted by the participants in their responses was the need to expand the scope of the MAF tool. The most suggested strategy was that of broad information, as they believed in promoting knowledge, as a means to valorize abilities, inclusion, and effective participation of people with disabilities [ 8 , 9 , 11 ]. This possibility was mentioned to reach a broad and diversified audience.

Still within the context of Theme 1, participants’ perceptions highlight the importance of extending the reach of the MAF tool to other environments frequented by typical and atypical children, to better serve the purpose of increasing engagement, fostering more effective participation, and creating new and promising perspectives for children and adolescents with disabilities [ 20 , 21 ]. In this sense, its use in the school environment was identified as particularly notable. Regardless of the presence of disability, respondents indicated the use for everyone, aiming to promote better inclusion and integration of children and adolescents with disabilities with their peers.

Thus, we emphasize that the implementation of the MAF tool was referred to as desirable, useful, and recommended. However, it requires widespread dissemination, use in various contexts, and an expanded target audience.

In the scope of Theme 2, entitled ’Barriers and Facilitators for the Implementation Process of MAF,’ the emphasis was on the importance of promoting behavioral changes and attitude changes. Participants pointed out barriers related to disability and the life of people with disabilities. These barriers were associated with the implementation of the MAF tool and the means to address them, among others, through health education.

The need to instigate governmental actions was expressed in the study. The allocation of government resources can be positive for applying the principles of equity, justice, and respect for human rights. Additionally, it can promote process efficiency, the exchange of knowledge and experiences, as well as ensure an integrated and holistic approach to promoting broad access to health and inclusion with effective participation of people with disabilities, corroborating with proposals by Schiariti and McWilliam (2021) [ 2 ] and Schiariti (2020) [ 6 ].

Therefore, professionals and families, by emphasizing the importance of health education, underscored the need to transform discussions about the implementation of MAF into concrete practices, beyond the theoretical scope and implementation in healthcare institutions. This argument was echoed in the statements of the interviewees, advocating for broadening perspectives and adopting positive health attitudes. Consequently, the aim is to maximize functional aspects and abilities while minimizing the constant focus on highlighting the disability. These considerations find support in the World Health Organization’s (WHO) Convention on the Rights of Persons with Disabilities, which highlights that positive attitudes in the field of health play a fundamental role in encouraging innovation and empowering people with disabilities [ 18 ].

The implementation of the MAF tool may face challenges, with resistance to attitude changes being one of the most significant, especially when prioritizing the recognition of the abilities of people with disabilities over a disability-focused approach. This process requires a deeper understanding of the MAF tool and the development of strategies for the effective use of the My Abilities ID CARD. To overcome these challenges, actions such as continuous education for professionals and health education for healthcare service users, as well as for the general population, play crucial roles. These measures not only support the adoption of the most appropriate strategies for the implementation process but also promote a broader and more inclusive understanding of the abilities of people with disabilities.

However, the study revealed new insights at each stage. This brought additional perspectives from the interviewees to light, such as the implementation of MAF in various healthcare centers and schools, to meet the need for an effective transition from disability-focused approaches to approaches that promote the rights of people with disabilities, which is in line with Schiariti and McWilliam (2021), Schiariti (2020), and Liao et al. (2023) [ 2 , 6 , 10 ]. Additionally, when interviewees mention the importance of policy-related actions, they hope to ensure that children/adolescents with disabilities can showcase their abilities, regardless of the nature of the disability, and without the constant need to prove relevant or specific degrees of incapacity to secure their rights.

Final considerations

The discussions centered on the perceptions of a heterogeneous group of participants regarding the implementation process of the My Abilities First (MAF) tool were conducive to new analyses, thereby enriching the research journey. In this context, observations arose that had not previously been considered by the researchers. These observations are related to the need to expand the use and acceptance of the MAF proposal through restructuring the tool for use in different contexts and through health education actions.

The innovative, accessible, and low-cost approach proposed by the tool focuses on valuing the abilities of people with disabilities and proves to be an achievable and promising process for improving clinical practice in healthcare, changing perceptions, behaviors, and contributing to addressing the barriers associated with disability and the lives of people with disabilities. In summary, this formed the basis for the study, which also provided new perspectives and interpretations on the implementation process of the MAF tool, such as expanding implementation beyond the healthcare context.

Based on these results and considering the limitations outlined in the study, the lack of greater diversity in the sample in terms of cultural, socioeconomic, and primarily geographical differences, as well as the absence of cross-cultural adaptation of the MAF tool, may have affected obtaining a better perspective on the results. These two aspects may be crucial for promising directions in future research.

Furthermore, other important perspectives to be included in future studies include the formulation of strategies for the use of the My Abilities ID CARD and the development of protocols or implementation manuals for the MAF.

Supporting information

https://doi.org/10.1371/journal.pone.0301718.s001

Acknowledgments

We would like to thank the Brazilian Coordination for the Improvement of Higher Education Personnel, the Secretariat Public Health of Rio Grande do Norte and the Postgraduate Program in Physiotherapy at UFRN.

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• Published: 11 May 2024

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Understanding the stressors and coping strategies of nursing students in their first clinical training is important for improving student performance, helping students develop a professional identity and problem-solving skills, and improving the clinical teaching aspects of the curriculum in nursing programmes. While previous research have examined nurses’ sources of stress and coping styles in the Arab region, there is limited understanding of these stressors and coping strategies of nursing students within the UAE context thereby, highlighting the novelty and significance of the study.

A qualitative study was conducted using semi-structured interviews. Overall 30 students who were undergoing their first clinical placement in Year 2 at the University of Sharjah between May and June 2022 were recruited. All interviews were recorded and transcribed verbatim and analyzed for themes.

During their first clinical training, nursing students are exposed to stress from different sources, including the clinical environment, unfriendly clinical tutors, feelings of disconnection, multiple expectations of clinical staff and patients, and gaps between the curriculum of theory classes and labatories skills and students’ clinical experiences. We extracted three main themes that described students’ stress and use of coping strategies during clinical training: (1) managing expectations; (2) theory-practice gap; and (3) learning to cope. Learning to cope, included two subthemes: positive coping strategies and negative coping strategies.

Conclusions

This qualitative study sheds light from the students viewpoint about the intricate interplay between managing expectations, theory practice gap and learning to cope. Therefore, it is imperative for nursing faculty, clinical agencies and curriculum planners to ensure maximum learning in the clinical by recognizing the significance of the stressors encountered and help students develop positive coping strategies to manage the clinical stressors encountered. Further research is required look at the perspective of clinical stressors from clinical tutors who supervise students during their first clinical practicum.

Peer Review reports

Nursing education programmes aim to provide students with high-quality clinical learning experiences to ensure that nurses can provide safe, direct care to patients [ 1 ]. The nursing baccalaureate programme at the University of Sharjah is a four year program with 137 credits. The programmes has both theoretical and clinical components withs nine clinical courses spread over the four years The first clinical practicum which forms the basis of the study takes place in year 2 semester 2.

Clinical practice experience is an indispensable component of nursing education and links what students learn in the classroom and in skills laboratories to real-life clinical settings [ 2 , 3 , 4 ]. However, a gap exists between theory and practice as the curriculum in the classroom differs from nursing students’ experiences in the clinical nursing practicum [ 5 ]. Clinical nursing training places (or practicums, as they are commonly referred to), provide students with the necessary experiences to ensure that they become proficient in the delivery of patient care [ 6 ]. The clinical practicum takes place in an environment that combines numerous structural, psychological, emotional and organizational elements that influence student learning [ 7 ] and may affect the development of professional nursing competencies, such as compassion, communication and professional identity [ 8 ]. While clinical training is a major component of nursing education curricula, stress related to clinical training is common among students [ 9 ]. Furthermore, the nursing literature indicates that the first exposure to clinical learning is one of the most stressful experiences during undergraduate studies [ 8 , 10 ]. Thus, the clinical component of nursing education is considered more stressful than the theoretical component. Students often view clinical learning, where most learning takes place, as an unsupportive environment [ 11 ]. In addition, they note strained relationships between themselves and clinical preceptors and perceive that the negative attitudes of clinical staff produce stress [ 12 ].

The effects of stress on nursing students often involve a sense of uncertainty, uneasiness, or anxiety. The literature is replete with evidence that nursing students experience a variety of stressors during their clinical practicum, beginning with the first clinical rotation. Nursing is a complex profession that requires continuous interaction with a variety of individuals in a high-stress environment. Stress during clinical learning can have multiple negative consequences, including low academic achievement, elevated levels of burnout, and diminished personal well-being [ 13 , 14 ]. In addition, both theoretical and practical research has demonstrated that increased, continual exposure to stress leads to cognitive deficits, inability to concentrate, lack of memory or recall, misinterpretation of speech, and decreased learning capacity [ 15 ]. Furthermore, stress has been identified as a cause of attrition among nursing students [ 16 ].

Most sources of stress have been categorized as academic, clinical or personal. Each person copes with stress differently [ 17 ], and utilizes deliberate, planned, and psychological efforts to manage stressful demands [ 18 ]. Coping mechanisms are commonly termed adaptation strategies or coping skills. Labrague et al. [ 19 ] noted that students used critical coping strategies to handle stress and suggested that problem solving was the most common coping or adaptation mechanism used by nursing students. Nursing students’ coping strategies affect their physical and psychological well-being and the quality of nursing care they offer. Therefore, identifying the coping strategies that students use to manage stressors is important for early intervention [ 20 ].

Studies on nursing students’ coping strategies have been conducted in various countries. For example, Israeli nursing students were found to adopt a range of coping mechanisms, including talking to friends, engaging in sports, avoiding stress and sadness/misery, and consuming alcohol [ 21 ]. Other studies have examined stress levels among medical students in the Arab region. Chaabane et al. [ 15 ], conducted a systematic review of sudies in Arab countries, including Saudi Arabia, Egypt, Jordan, Iraq, Pakistan, Oman, Palestine and Bahrain, and reported that stress during clinical practicums was prevalent, although it could not be determined whether this was limited to the initial clinical course or occurred throughout clinical training. Stressors highlighted during the clinical period in the systematic review included assignments and workload during clinical practice, a feeling that the requirements of clinical practice exceeded students’ physical and emotional endurance and that their involvement in patient care was limited due to lack of experience. Furthermore, stress can have a direct effect on clinical performance, leading to mental disorders. Tung et al. [ 22 ], reported that the prevalence of depression among nursing students in Arab countries is 28%, which is almost six times greater than the rest of the world [ 22 ]. On the other hand, Saifan et al. [ 5 ], explored the theory-practice gap in the United Arab Emirates and found that clinical stressors could be decreased by preparing students better for clinical education with qualified clinical faculty and supportive preceptors.

The purpose of this study was to identify the stressors experienced by undergraduate nursing students in the United Arab Emirates during their first clinical training and the basic adaptation approaches or coping strategies they used. Recognizing or understanding different coping processes can inform the implementation of corrective measures when students experience clinical stress. The findings of this study may provide valuable information for nursing programmes, nurse educators, and clinical administrators to establish adaptive strategies to reduce stress among students going clinical practicums, particularly stressors from their first clinical training in different healthcare settings.

A qualitative approach was adopted to understand clinical stressors and coping strategies from the perspective of nurses’ lived experience. Qualitative content analysis was employed to obtain rich and detailed information from our qualitative data. Qualitative approaches seek to understand the phenomenon under study from the perspectives of individuals with lived experience [ 23 ]. Qualitative content analysis is an interpretive technique that examines the similarities and differences between and within different areas of text while focusing on the subject [ 24 ]. It is used to examine communication patterns in a repeatable and systematic way [ 25 ] and yields rich and detailed information on the topic under investigation [ 23 ]. It is a method of systematically coding and categorizing information and comprises a process of comprehending, interpreting, and conceptualizing the key meanings from qualitative data [ 26 ].

Setting and participants

This study was conducted after the clinical rotations ended in April 2022, between May and June in the nursing programme at the College of Health Sciences, University of Sharjah, in the United Arab Emirates. The study population comprised undergraduate nursing students who were undergoing their first clinical training and were recruited using purposive sampling. The inclusion criteria for this study were second-year nursing students in the first semester of clinical training who could speak English, were willing to participate in this research, and had no previous clinical work experience. The final sample consisted of 30 students.

Research instrument

The research instrument was a semi structured interview guide. The interview questions were based on an in-depth review of related literature. An intensive search included key words in Google Scholar, PubMed like the terms “nursing clinical stressors”, “nursing students”, and “coping mechanisms”. Once the questions were created, they were validated by two other faculty members who had relevant experience in mental health. A pilot test was conducted with five students and based on their feedback the following research questions, which were addressed in the study.

How would you describe your clinical experiences during your first clinical rotations?

In what ways did you find the first clinical rotation to be stressful?

What factors hindered your clinical training?

How did you cope with the stressors you encountered in clinical training?

Which strategies helped you cope with the clinical stressors you encountered?

Data collection

Semi-structured interviews were chosen as the method for data collection. Semi structured interviews are a well-established approach for gathering data in qualitative research and allow participants to discuss their views, experiences, attitudes, and beliefs in a positive environment [ 27 ]. This approach allows for flexibility in questioning thereby ensuring that key topics related to clinical learning stressors and coping strategies would be explored. Participants were given the opportunity to express their views, experiences, attitudes, and beliefs in a positive environment, encouraging open communication. These semi structured interviews were conducted by one member of the research team (MAS) who had a mental health background, and another member of the research team who attended the interviews as an observer (JMD). Neither of these researchers were involved in teaching the students during their clinical practicum, which helped to minimize bias. The interviews took place at the University of Sharjah, specifically in building M23, providing a familiar and comfortable environment for the participant. Before the interviews were all students who agreed to participate were provided with an explanation of the study’s purpose. The time and location of each interview were arranged. Before the interviews were conducted, all students who provided consent to participate received an explanation of the purpose of the study, and the time and place of each interview were arranged to accommodate the participants’ schedules and preferences. The interviews were conducted after the clinical rotation had ended in April, and after the final grades had been submitted to the coordinator. The timings of the interviews included the month of May and June which ensured that participants have completed their practicum experience and could reflect on the stressors more comprehensively. The interviews were audio-recorded with the participants’ consent, and each interview lasted 25–40 min. The data were collected until saturation was reached for 30 students. Memos and field notes were also recorded as part of the data collection process. These additional data allowed for triangulation to improve the credibility of the interpretations of the data [ 28 ]. Memos included the interviewers’ thoughts and interpretations about the interviews, the research process (including questions and gaps), and the analytic progress used for the research. Field notes were used to record the interviewers’ observations and reflections on the data. These additional data collection methods were important to guide the researchers in the interpretation of the data on the participants’ feelings, perspectives, experiences, attitudes, and beliefs. Finally, member checking was performed to ensure conformability.

Data analysis

The study used the content analysis method proposed by Graneheim and Lundman [ 24 ]. According to Graneheim and Lundman [ 24 ], content analysis is an interpretive technique that examines the similarities and differences between distinct parts of a text. This method allows researchers to determine exact theoretical and operational definitions of words, phrases, and symbols by elucidating their constituent properties [ 29 ]. First, we read the interview transcripts several times to reach an overall understanding of the data. All verbatim transcripts were read several times and discussed among all authors. We merged and used line-by-line coding of words, sentences, and paragraphs relevant to each other in terms of both the content and context of stressors and coping mechanisms. Next, we used data reduction to assess the relationships among themes using tables and diagrams to indicate conceptual patterns. Content related to stress encountered by students was extracted from the transcripts. In a separate document, we integrated and categorized all words and sentences that were related to each other in terms of both content and context. We analyzed all codes and units of meaning and compared them for similarities and differences in the context of this study. Furthermore, the emerging findings were discussed with other members of the researcher team. The final abstractions of meaningful subthemes into themes were discussed and agreed upon by the entire research team. This process resulted in the extraction of three main themes in addition to two subthemes related to stress and coping strategies.

Ethical considerations

The University of Sharjah Research Ethics Committee provided approval to conduct this study (Reference Number: REC 19-12-03-01-S). Before each interview, the goal and study procedures were explained to each participant, and written informed consent was obtained. The participants were informed that participation in the study was voluntary and that they could withdraw from the study at any time. In the event they wanted to withdraw from the study, all information related to the participant would be removed. No participant withdrew from the study. Furthermore, they were informed that their clinical practicum grade would not be affected by their participation in this study. We chose interview locations in Building M23that were private and quiet to ensure that the participants felt at ease and confident in verbalizing their opinions. No participant was paid directly for involvement in this study. In addition, participants were assured that their data would remain anonymous and confidential. Confidentiality means that the information provided by participants was kept private with restrictions on how and when data can be shared with others. The participants were informed that their information would not be duplicated or disseminated without their permission. Anonymity refers to the act of keeping people anonymous with respect to their participation in a research endeavor. No personal identifiers were used in this study, and each participant was assigned a random alpha-numeric code (e.g., P1 for participant 1). All digitally recorded interviews were downloaded to a secure computer protected by the principal investigator with a password. The researchers were the only people with access to the interview material (recordings and transcripts). All sensitive information and materials were kept secure in the principal researcher’s office at the University of Sharjah. The data will be maintained for five years after the study is completed, after which the material will be destroyed (the transcripts will be shredded, and the tapes will be demagnetized).

In total, 30 nursing students who were enrolled in the nursing programme at the Department of Nursing, College of Health Sciences, University of Sharjah, and who were undergoing their first clinical practicum participated in the study. Demographically, 80% ( n  = 24) were females and 20% ( n  = 6) were male participants. The majority (83%) of study participants ranged in age from 18 to 22 years. 20% ( n  = 6) were UAE nationals, 53% ( n  = 16) were from Gulf Cooperation Council countries, while 20% ( n  = 6) hailed from Africa and 7% ( n  = 2) were of South Asian descent. 67% of the respondents lived with their families while 33% lived in the hostel. (Table  1 )

Following the content analysis, we identified three main themes: (1) managing expectations, (2) theory-practice gap and 3)learning to cope. Learning to cope had two subthemes: positive coping strategies and negative coping strategies. An account of each theme is presented along with supporting excerpts for the identified themes. The identified themes provide valuable insight into the stressors encountered by students during their first clinical practicum. These themes will lead to targeted interventions and supportive mechanisms that can be built into the clinical training curriculum to support students during clinical practice.

Theme 1: managing expectations

In our examination of the stressors experienced by nursing students during their first clinical practicum and the coping strategies they employed, we identified the first theme as managing expectations.

The students encountered expectations from various parties, such as clinical staff, patients and patients’ relatives which they had to navigate. They attempted to fulfil their expectations as they progressed through training, which presented a source of stress. The students noted that the hospital staff and patients expected them to know how to perform a variety of tasks upon request, which made the students feel stressed and out of place if they did not know how to perform these tasks. Some participants noted that other nurses in the clinical unit did not allow them to participate in nursing procedures, which was considered an enormous impediment to clinical learning, as noted in the excerpt below:

“…Sometimes the nurses… They will not allow us to do some procedures or things during clinical. And sometimes the patients themselves don’t allow us to do procedures” (P5).

Some of the students noted that they felt they did not belong and felt like foreigners in the clinical unit. Excerpts from the students are presented in the following quotes;

“The clinical environment is so stressful. I don’t feel like I belong. There is too little time to build a rapport with hospital staff or the patient” (P22).

“… you ask the hospital staff for some guidance or the location of equipment, and they tell us to ask our clinical tutor …but she is not around … what should I do? It appears like we do not belong, and the sooner the shift is over, the better” (P18).

“The staff are unfriendly and expect too much from us students… I feel like I don’t belong, or I am wasting their (the hospital staff’s) time. I want to ask questions, but they have loads to do” (P26).

Other students were concerned about potential failure when working with patients during clinical training, which impacted their confidence. They were particularly afraid of failure when performing any clinical procedures.

“At the beginning, I was afraid to do procedures. I thought that maybe the patient would be hurt and that I would not be successful in doing it. I have low self-confidence in doing procedures” (P13).

The call bell rings, and I am told to answer Room No. XXX. The patient wants help to go to the toilet, but she has two IV lines. I don’t know how to transport the patient… should I take her on the wheelchair? My eyes glance around the room for a wheelchair. I am so confused …I tell the patient I will inform the sister at the nursing station. The relative in the room glares at me angrily … “you better hurry up”…Oh, I feel like I don’t belong, as I am not able to help the patient… how will I face the same patient again?” (P12).

Another major stressor mentioned in the narratives was related to communication and interactions with patients who spoke another language, so it was difficult to communicate.

“There was a challenge with my communication with the patients. Sometimes I have communication barriers because they (the patients) are of other nationalities. I had an experience with a patient [who was] Indian, and he couldn’t speak my language. I did not understand his language” (P9).

Thus, a variety of expectations from patients, relatives, hospital staff, and preceptors acted as sources of stress for students during their clinical training.

Theme 2: theory-practice gap

Theory-practice gaps have been identified in previous studies. In our study, there was complete dissonance between theory and actual clinical practice. The clinical procedures or practices nursing students were expected to perform differed from the theory they had covered in their university classes and skills lab. This was described as a theory–practice gap and often resulted in stress and confusion.

“For example …the procedures in the hospital are different. They are different from what we learned or from theory on campus. Or… the preceptors have different techniques than what we learned on campus. So, I was stress[ed] and confused about it” (P11).

Furthermore, some students reported that they did not feel that they received adequate briefing before going to clinical training. A related source of stress was overload because of the volume of clinical coursework and assignments in addition to clinical expectations. Additionally, the students reported that a lack of time and time management were major sources of stress in their first clinical training and impacted their ability to complete the required paperwork and assignments:

“…There is not enough time…also, time management at the hospital…for example, we start at seven a.m., and the handover takes 1 hour to finish. They (the nurses at the hospital) are very slow…They start with bed making and morning care like at 9.45 a.m. Then, we must fill [out] our assessment tool and the NCP (nursing care plan) at 10 a.m. So, 15 only minutes before going to our break. We (the students) cannot manage this time. This condition makes me and my friends very stressed out. -I cannot do my paperwork or assignments; no time, right?” (P10).

“Stressful. There is a lot of work to do in clinical. My experiences are not really good with this course. We have a lot of things to do, so many assignments and clinical procedures to complete” (P16).

The participants noted that the amount of required coursework and number of assignments also presented a challenge during their first clinical training and especially affected their opportunity to learn.

“I need to read the file, know about my patient’s condition and pathophysiology and the rationale for the medications the patient is receiving…These are big stressors for my learning. I think about assignments often. Like, we are just focusing on so many assignments and papers. We need to submit assessments and care plans for clinical cases. We focus our time to complete and finish the papers rather than doing the real clinical procedures, so we lose [the] chance to learn” (P25).

Another participant commented in a similar vein that there was not enough time to perform tasks related to clinical requirements during clinical placement.

“…there is a challenge because we do not have enough time. Always no time for us to submit papers, to complete assessment tools, and some nurses, they don’t help us. I think we need more time to get more experiences and do more procedures, reduce the paperwork that we have to submit. These are challenges …” (P14).

There were expectations that the students should be able to carry out their nursing duties without becoming ill or adversely affected. In addition, many students reported that the clinical environment was completely different from the skills laboratory at the college. Exposure to the clinical setting added to the theory-practice gap, and in some instances, the students fell ill.

One student made the following comment:

“I was assisting a doctor with a dressing, and the sight and smell from the oozing wound was too much for me. I was nauseated. As soon as the dressing was done, I ran to the bathroom and threw up. I asked myself… how will I survive the next 3 years of nursing?” (P14).

Theme 3: learning to cope

The study participants indicated that they used coping mechanisms (both positive and negative) to adapt to and manage the stressors in their first clinical practicum. Important strategies that were reportedly used to cope with stress were time management, good preparation for clinical practice, and positive thinking as well as engaging in physical activity and self-motivation.

“Time management. Yes, it is important. I was encouraging myself. I used time management and prepared myself before going to the clinical site. Also, eating good food like cereal…it helps me very much in the clinic” (P28).

“Oh yeah, for sure positive thinking. In the hospital, I always think positively. Then, after coming home, I get [to] rest and think about positive things that I can do. So, I will think something good [about] these things, and then I will be relieved of stress” (P21).

Other strategies commonly reported by the participants were managing their breathing (e.g., taking deep breaths, breathing slowly), taking breaks to relax, and talking with friends about the problems they encountered.

“I prefer to take deep breaths and breathe slowly and to have a cup of coffee and to talk to my friends about the case or the clinical preceptor and what made me sad so I will feel more relaxed” (P16).

“Maybe I will take my break so I feel relaxed and feel better. After clinical training, I go directly home and take a long shower, going over the day. I will not think about anything bad that happened that day. I just try to think about good things so that I forget the stress” (P27).

“Yes, my first clinical training was not easy. It was difficult and made me stressed out…. I felt that it was a very difficult time for me. I thought about leaving nursing” (P7).

I was not able to offer my prayers. For me, this was distressing because as a Muslim, I pray regularly. Now, my prayer time is pushed to the end of the shift” (P11).

“When I feel stress, I talk to my friends about the case and what made me stressed. Then I will feel more relaxed” (P26).

Self-support or self-motivation through positive self-talk was also used by the students to cope with stress.

“Yes, it is difficult in the first clinical training. When I am stress[ed], I go to the bathroom and stand in the front of the mirror; I talk to myself, and I say, “You can do it,” “you are a great student.” I motivate myself: “You can do it”… Then, I just take breaths slowly several times. This is better than shouting or crying because it makes me tired” (P11).

Other participants used physical activity to manage their stress.

“How do I cope with my stress? Actually, when I get stressed, I will go for a walk on campus” (P4).

“At home, I will go to my room and close the door and start doing my exercises. After that, I feel the negative energy goes out, then I start to calm down… and begin my clinical assignments” (P21).

Both positive and negative coping strategies were utilized by the students. Some participants described using negative coping strategies when they encountered stress during their clinical practice. These negative coping strategies included becoming irritable and angry, eating too much food, drinking too much coffee, and smoking cigarettes.

“…Negative adaptation? Maybe coping. If I am stressed, I get so angry easily. I am irritable all day also…It is negative energy, right? Then, at home, I am also angry. After that, it is good to be alone to think about my problems” (P12).

“Yeah, if I…feel stress or depressed, I will eat a lot of food. Yeah, ineffective, like I will be eating a lot, drinking coffee. Like I said, effective, like I will prepare myself and do breathing, ineffective, I will eat a lot of snacks in between my free time. This is the bad side” (P16).

“…During the first clinical practice? Yes, it was a difficult experience for us…not only me. When stressed, during a break at the hospital, I will drink two or three cups of coffee… Also, I smoke cigarettes… A lot. I can drink six cups [of coffee] a day when I am stressed. After drinking coffee, I feel more relaxed, I finish everything (food) in the refrigerator or whatever I have in the pantry, like chocolates, chips, etc” (P23).

These supporting excerpts for each theme and the analysis offers valuable insights into the specific stressors faced by nursing students during their first clinical practicum. These insights will form the basis for the development of targeted interventions and supportive mechanisms within the clinical training curriculum to better support students’ adjustment and well-being during clinical practice.

Our study identified the stressors students encounter in their first clinical practicum and the coping strategies, both positive and negative, that they employed. Although this study emphasizes the importance of clinical training to prepare nursing students to practice as nurses, it also demonstrates the correlation between stressors and coping strategies.The content analysis of the first theme, managing expectations, paves the way for clinical agencies to realize that the students of today will be the nurses of tomorrow. It is important to provide a welcoming environment where students can develop their identities and learn effectively. Additionally, clinical staff should foster an environment of individualized learning while also assisting students in gaining confidence and competence in their repertoire of nursing skills, including critical thinking, problem solving and communication skills [ 8 , 15 , 19 , 30 ]. Another challenge encountered by the students in our study was that they were prevented from participating in clinical procedures by some nurses or patients. This finding is consistent with previous studies reporting that key challenges for students in clinical learning include a lack of clinical support and poor attitudes among clinical staff and instructors [ 31 ]. Clinical staff with positive attitudes have a positive impact on students’ learning in clinical settings [ 32 ]. The presence, supervision, and guidance of clinical instructors and the assistance of clinical staff are essential motivating components in the clinical learning process and offer positive reinforcement [ 30 , 33 , 34 ]. Conversely, an unsupportive learning environment combined with unwelcoming clinical staff and a lack of sense of belonging negatively impact students’ clinical learning [ 35 ].

The sources of stress identified in this study were consistent with common sources of stress in clinical training reported in previous studies, including the attitudes of some staff, students’ status in their clinical placement and educational factors. Nursing students’ inexperience in the clinical setting and lack of social and emotional experience also resulted in stress and psychological difficulties [ 36 ]. Bhurtun et al. [ 33 ] noted that nursing staff are a major source of stress for students because the students feel like they are constantly being watched and evaluated.

We also found that students were concerned about potential failure when working with patients during their clinical training. Their fear of failure when performing clinical procedures may be attributable to low self-confidence. Previous studies have noted that students were concerned about injuring patients, being blamed or chastised, and failing examinations [ 37 , 38 ]. This was described as feeling “powerless” in a previous study [ 7 , 12 ]. In addition, patients’ attitudes towards “rejecting” nursing students or patients’ refusal of their help were sources of stress among the students in our study and affected their self-confidence. Self-confidence and a sense of belonging are important for nurses’ personal and professional identity, and low self-confidence is a problem for nursing students in clinical learning [ 8 , 39 , 40 ]. Our findings are consistent with a previous study that reported that a lack of self-confidence was a primary source of worry and anxiety for nursing students and affected their communication and intention to leave nursing [ 41 ].

In the second theme, our study suggests that students encounter a theory-practice gap in clinical settings, which creates confusion and presents an additional stressors. Theoretical and clinical training are complementary elements of nursing education [ 40 ], and this combination enables students to gain the knowledge, skills, and attitudes necessary to provide nursing care. This is consistent with the findings of a previous study that reported that inconsistencies between theoretical knowledge and practical experience presented a primary obstacle to the learning process in the clinical context [ 42 ], causing students to lose confidence and become anxious [ 43 ]. Additionally, the second theme, the theory-practice gap, authenticates Safian et al.’s [ 5 ] study of the theory-practice gap that exists United Arab Emirates among nursing students as well as the need for more supportive clinical faculty and the extension of clinical hours. The need for better time availability and time management to complete clinical tasks were also reported by the students in the study. Students indicated that they had insufficient time to complete clinical activities because of the volume of coursework and assignments. Our findings support those of Chaabane et al. [ 15 ]. A study conducted in Saudi Arabia [ 44 ] found that assignments and workload were among the greatest sources of stress for students in clinical settings. Effective time management skills have been linked to academic achievement, stress reduction, increased creativity [ 45 ], and student satisfaction [ 46 ]. Our findings are also consistent with previous studies that reported that a common source of stress among first-year students was the increased classroom workload [ 19 , 47 ]. As clinical assignments and workloads are major stressors for nursing students, it is important to promote activities to help them manage these assignments [ 48 ].

Another major challenge reported by the participants was related to communicating and interacting with other nurses and patients. The UAE nursing workforce and population are largely expatriate and diverse and have different cultural and linguistic backgrounds. Therefore, student nurses encounter difficulty in communication [ 49 ]. This cultural diversity that students encounter in communication with patients during clinical training needs to be addressed by curriculum planners through the offering of language courses and courses on cultural diversity [ 50 ].

Regarding the third and final theme, nursing students in clinical training are unable to avoid stressors and must learn to cope with or adapt to them. Previous research has reported a link between stressors and the coping mechanisms used by nursing students [ 51 , 52 , 53 ]. In particular, the inability to manage stress influences nurses’ performance, physical and mental health, attitude, and role satisfaction [ 54 ]. One such study suggested that nursing students commonly use problem-focused (dealing with the problem), emotion-focused (regulating emotion), and dysfunctional (e.g., venting emotions) stress coping mechanisms to alleviate stress during clinical training [ 15 ]. Labrague et al. [ 51 ] highlighted that nursing students use both active and passive coping techniques to manage stress. The pattern of clinical stress has been observed in several countries worldwide. The current study found that first-year students experienced stress during their first clinical training [ 35 , 41 , 55 ]. The stressors they encountered impacted their overall health and disrupted their clinical learning. Chaabane et al. [ 15 ] reported moderate and high stress levels among nursing students in Bahrain, Egypt, Iraq, Jordan, Oman, Pakistan, Palestine, Saudi Arabia, and Sudan. Another study from Bahrain reported that all nursing students experienced moderate to severe stress in their first clinical placement [ 56 ]. Similarly, nursing students in Spain experienced a moderate level of stress, and this stress was significantly correlated with anxiety [ 30 ]. Therefore, it is imperative that pastoral systems at the university address students’ stress and mental health so that it does not affect their clinical performance. Faculty need to utilize evidence-based interventions to support students so that anxiety-producing situations and attrition are minimized.

In our study, students reported a variety of positive and negative coping mechanisms and strategies they used when they experienced stress during their clinical practice. Positive coping strategies included time management, positive thinking, self-support/motivation, breathing, taking breaks, talking with friends, and physical activity. These findings are consistent with those of a previous study in which healthy coping mechanisms used by students included effective time management, social support, positive reappraisal, and participation in leisure activities [ 57 ]. Our study found that relaxing and talking with friends were stress management strategies commonly used by students. Communication with friends to cope with stress may be considered social support. A previous study also reported that people seek social support to cope with stress [ 58 ]. Some students in our study used physical activity to cope with stress, consistent with the findings of previous research. Stretching exercises can be used to counteract the poor posture and positioning associated with stress and to assist in reducing physical tension. Promoting such exercise among nursing students may assist them in coping with stress in their clinical training [ 59 ].

Our study also showed that when students felt stressed, some adopted negative coping strategies, such as showing anger/irritability, engaging in unhealthy eating habits (e.g., consumption of too much food or coffee), or smoking cigarettes. Previous studies have reported that high levels of perceived stress affect eating habits [ 60 ] and are linked to poor diet quality, increased snacking, and low fruit intake [ 61 ]. Stress in clinical settings has also been linked to sleep problems, substance misuse, and high-risk behaviors’ and plays a major role in student’s decision to continue in their programme.

Implications of the study

The implications of the study results can be grouped at multiple levels including; clinical, educational, and organizational level. A comprehensive approach to addressing the stressors encountered by nursing students during their clinical practicum can be overcome by offering some practical strategies to address the stressors faced by nursing students during their clinical practicum. By integrating study findings into curriculum planning, mentorship programs, and organizational support structures, a supportive and nurturing environment that enhances students’ learning, resilience, and overall success can be envisioned.

Clinical level

Introducing simulation in the skills lab with standardized patients and the use of moulage to demonstrate wounds, ostomies, and purulent dressings enhances students’ practical skills and prepares them for real-world clinical scenarios. Organizing orientation days at clinical facilities helps familiarize students with the clinical environment, identify potential stressors, and introduce interventions to enhance professionalism, social skills, and coping abilities Furthermore, creating a WhatsApp group facilitates communication and collaboration among hospital staff, clinical tutors, nursing faculty, and students, enabling immediate support and problem-solving for clinical situations as they arise, Moreover, involving chief nursing officers of clinical facilities in the Nursing Advisory Group at the Department of Nursing promotes collaboration between academia and clinical practice, ensuring alignment between educational objectives and the needs of the clinical setting [ 62 ].

Educational level

Sharing study findings at conferences (we presented the results of this study at Sigma Theta Tau International in July 2023 in Abu Dhabi, UAE) and journal clubs disseminates knowledge and best practices among educators and clinicians, promoting awareness and implementation of measures to improve students’ learning experiences. Additionally we hold mentorship training sessions annually in January and so we shared with the clinical mentors and preceptors the findings of this study so that they proactively they are equipped with strategies to support students’ coping with stressors during clinical placements.

Organizational level

At the organizational we relooked at the available student support structures, including counseling, faculty advising, and career advice, throughout the nursing program emphasizing the importance of holistic support for students’ well-being and academic success as well as retention in the nursing program. Also, offering language courses as electives recognizes the value of communication skills in nursing practice and provides opportunities for personal and professional development.

For first-year nursing students, clinical stressors are inevitable and must be given proper attention. Recognizing nursing students’ perspectives on the challenges and stressors experienced in clinical training is the first step in overcoming these challenges. In nursing schools, providing an optimal clinical environment as well as increasing supervision and evaluation of students’ practices should be emphasized. Our findings demonstrate that first-year nursing students are exposed to a variety of different stressors. Identifying the stressors, pressures, and obstacles that first-year students encounter in the clinical setting can assist nursing educators in resolving these issues and can contribute to students’ professional development and survival to allow them to remain in the profession. To overcome stressors, students frequently employ problem-solving approaches or coping mechanisms. The majority of nursing students report stress at different levels and use a variety of positive and negative coping techniques to manage stress.

The present results may not be generalizable to other nursing institutions because this study used a purposive sample along with a qualitative approach and was limited to one university in the Middle East. Furthermore, the students self-reported their stress and its causes, which may have introduced reporting bias. The students may also have over or underreported stress or coping mechanisms because of fear of repercussions or personal reasons, even though the confidentiality of their data was ensured. Further studies are needed to evaluate student stressors and coping now that measures have been introduced to support students. Time will tell if these strategies are being used effectively by both students and clinical personnel or if they need to be readdressed. Finally, we need to explore the perceptions of clinical faculty towards supervising students in their first clinical practicum so that clinical stressors can be handled effectively.

Data availability

The data sets are available with the corresponding author upon reasonable request.

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The authors are grateful to all second year nursing students who voluntarily participated in the study.

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Jacqueline Maria Dias, Muhammad Arsyad Subu, Nabeel Al-Yateem, Fatma Refaat Ahmed, Syed Azizur Rahman, Mini Sara Abraham, Sareh Mirza Forootan, Farzaneh Ahmad Sarkhosh & Fatemeh Javanbakh

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JMD conceptualized the idea and designed the methodology, formal analysis, writing original draft and project supervision and mentoring. MAS prepared the methodology and conducted the qualitative interviews and analyzed the methodology and writing of original draft and project supervision. NY, FRA, SAR, MSA writing review and revising the draft. SMF, FAS, FJ worked with MAS on the formal analysis and prepared the first draft.All authors reviewed the final manuscipt of the article.

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Dias, J.M., Subu, M.A., Al-Yateem, N. et al. Nursing students’ stressors and coping strategies during their first clinical training: a qualitative study in the United Arab Emirates. BMC Nurs 23 , 322 (2024). https://doi.org/10.1186/s12912-024-01962-5

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Exploring shared decision-making needs in lung cancer screening among high-risk groups and health care providers in China: a qualitative study

• Xiujing Lin 1 ,
• Fangfang Wang 1 ,
• Yonglin Li 1 ,
• Fang Lei 2 ,
• Weisheng Chen 3 ,
• Rachel H. Arbing 4 ,
• Wei-Ti Chen   ORCID: orcid.org/0000-0002-2342-045X 4 &
• Feifei Huang   ORCID: orcid.org/0000-0003-0197-8687 1  

BMC Cancer volume  24 , Article number:  613 ( 2024 ) Cite this article

Metrics details

The intricate balance between the advantages and risks of low-dose computed tomography (LDCT) impedes the utilization of lung cancer screening (LCS). Guiding shared decision-making (SDM) for well-informed choices regarding LCS is pivotal. There has been a notable increase in research related to SDM. However, these studies possess limitations. For example, they may ignore the identification of decision support and needs from the perspective of health care providers and high-risk groups. Additionally, these studies have not adequately addressed the complete SDM process, including pre-decisional needs, the decision-making process, and post-decision experiences. Furthermore, the East-West divide of SDM has been largely ignored. This study aimed to explore the decisional needs and support for shared decision-making for LCS among health care providers and high-risk groups in China.

Informed by the Ottawa Decision-Support Framework, we conducted qualitative, face-to-face in-depth interviews to explore shared decision-making among 30 lung cancer high-risk individuals and 9 health care providers. Content analysis was used for data analysis.

We identified 4 decisional needs that impair shared decision-making: (1) LCS knowledge deficit; (2) inadequate supportive resources; (3) shared decision-making conceptual bias; and (4) delicate doctor-patient bonds. We identified 3 decision supports: (1) providing information throughout the LCS process; (2) providing shared decision-making decision coaching; and (3) providing decision tools.

Conclusions

This study offers valuable insights into the decisional needs and support required to undergo LCS among high-risk individuals and perspectives from health care providers. Future studies should aim to design interventions that enhance the quality of shared decision-making by offering LCS information, decision tools for LCS, and decision coaching for shared decision-making (e.g., through community nurses). Simultaneously, it is crucial to assess individuals’ needs for effective deliberation to prevent conflicts and regrets after arriving at a decision.

Peer Review reports

Low-dose computed tomography (LDCT) is an effective tool for early lung cancer detection and has been proven to enhance survival rates in individuals at high-risk for lung cancer [ 1 , 2 ]. However, global LDCT usage is limited, with only 2-35% of eligible individuals undergoing screening [ 3 , 4 , 5 , 6 , 7 ], in contrast to 16-68% of eligible candidates undergoing colorectal cancer screening [ 8 ]. Improvements in LDCT screening rates for high-risk groups have been modest. The intricate balance between the advantages and risks of LDCT impedes the utilization of lung cancer screening (LCS) [ 9 ]. Notably, compared to their non-screened counterparts, high-risk individuals who underwent LDCT had a remarkable 24% decrease in lung cancer mortality [ 2 ]. However, the benefits of LDCT come with potential drawbacks, such as radiation-induced cancer, needless examinations, invasive procedures stemming from false positives, overdiagnosis, incidental discoveries, and psychological burdens [ 10 ]. These complexities render the LDCT screening decision-making process multifaceted and reliant on personal preferences. Hence, guiding high-risk groups toward well-informed choices regarding LCS is pivotal and represents a substantial mechanism for advancing the secondary prevention of lung cancer.

Shared decision-making is defined as “a collaborative approach for health care providers and patients in making informed health decisions”, which involves considering evidence regarding the benefits and risks of medical options, as well as individuals’ preferences and values [ 11 ]. This decision-making process allows both health care providers and individuals as well as their family members to engage in deliberation which leads to identifying the most appropriate decision for the situation [ 12 ]. Multiple guidelines strongly recommend shared decision-making as an essential step before patients undergo LDCT. Shared decision-making is also stipulated as a prerequisite for LDCT reimbursement by the Centers for Medicare and Medicaid Services in the United States [ 13 , 14 , 15 , 16 ]. Regrettably, the utilization of shared decision-making in clinical practice is currently not optimal [ 17 , 18 ]. Patients do not know what LDCT is, and they often report a lack of about the risks and benefits of LDCT. As a result, patients often have concerns about the risks of LDCT, and health care providers frequently fail to inquire about individuals’ preferences [ 19 ]. Consequently, there has been a notable increase in the literature focusing on barriers to shared decision-making from the perspectives of both health care providers and lung cancer high-risk groups. For example, studies have shown that the barriers to shared decision-making include different perceptions about the use of shared decision-making and a lack of time to communicate with providers. However, there are some limitations in terms of methodology and the comparative nature of the studies that focus on LCS shared decision-making. First, previously published studies focused on identifying barriers to shared decision-making and neglected decision support from physicians and patients. For instance, one study found that a lack of professionalism in health care providers is a barrier to shared decision-making, yet no studies have examined specific LCS shared decision-making decision supports for health care providers [ 19 ]. Second, current research centers on short-term decision-making experiences, such as cognitive consequences experienced immediately following shared decision-making. However, studies have not adequately addressed the complete shared decision-making process – pre-decisional needs, the decision-making process itself, and post-decision experiences, such as decision regret. Third, the COVID-19 pandemic has introduced a new risk of LDCT usage (exposure to the health-care environment) [ 20 ]. The added risk alters the benefit-risk ratio of LDCT under pre-COVID-19 guideline recommendations. Fourth, shared decision-making, developed in Western societies, is rarely discussed in China. The national climate and medical systems of China and Western countries differ greatly [ 21 ], and the lack of evidence on LCS shared decision-making in China indicates a need for an assessment of shared decision-making in those who require LDCT.

This study aimed to explore the decisional needs and decision support of shared decision-making for LCS among Chinese high-risk individuals and their health care providers using data collected through in-depth one-on-one interviews.

Theoretical framework

The Ottawa Decision-Support Framework (ODSF) is an evidence-based conceptual framework that is structured around three key components [ 22 ]: (1) assessing decisional needs, such as insufficient knowledge, complex decision types, and limited resources; (2) providing decision support, which encompasses clinical counseling, decision-making tools, and decision coaching; and (3) evaluating decisional outcomes, which includes assessing the quality of the decision-making process and its impact. According to the ODSF, successful decision support should be guided by an assessment of the individual’s knowledge and his/her ability to make his/her own decision to reduce their unmet needs and achieve a final health decision with the support of health care providers and family members. The ODSF has been successfully used within several populations with health needs to guide health decisions and provide decision support [ 23 , 24 ].

This qualitative study emphasizes the “who, what, and where” of events or experiences [ 25 ]. The central research question posed was, “What are the decisional needs and supports of LCS shared decision-making among individuals at high-risk of lung cancer and health care providers?” Consequently, a descriptive qualitative approach was deemed appropriate for exploring the decisional needs and supports for LCS shared decision-making among individuals at high-risk of lung cancer and health care providers [ 26 ]. This descriptive qualitative study adhered to the Consolidated Criteria for Reporting Qualitative Studies (COREQ) checklist [ 27 ]. Ethical approval for this study was obtained from the ethics committee of Fujian Medical University (Approval No. 2,023,098).

Inclusion and exclusion criteria

Aligned with the guidelines for the early detection of lung cancer in China [ 14 ], the inclusion criteria used for the high-risk group for lung cancer were as follows: (a) aged between 50 and 74 years; (b) had at least one of the following risk factors for lung cancer: a smoking history ≥ 30 pack-years, which includes current smokers or individuals who quit smoking within the last 15 years; prolonged exposure to passive smoking (living or working with smokers for 20 years or more); a history of COPD; a history of occupational exposure to asbestos, radon, beryllium, chromium, cadmium, nickel, silicon, soot, or coal soot for a minimum of 1 year; or a family history of lung cancer; (c) verbal confirmation of undergoing LCS shared decision-making; (d) undergone LDCT within the past 5 years; (e) Able to converse in Mandarin; (f) absence of cognitive or psychological disorders; and (g) willingness to share their personal stories. The exclusion criteria used for the high-risk group for lung cancer were as follows: (a) previous history of lung cancer; and (b) cognitive or psychological disorders (such as depression and anxiety). The inclusion criteria used for health care providers were as follows: (a) certified physicians or nurses; (b) expertise in LCS; and (c) willingness to share their experiences. Healthcare providers who were receiving external training were excluded from participation in the study.

Qualitative data collection

The data were collected from March 2023 to May 2023. A purposive sampling method was used to identify and recruit individuals at high-risk for lung cancer, as well as local health care providers from five community healthcare centers and two surgical oncology departments of tertiary hospitals. Study flyers provided information on the purpose of the study and the inclusion and exclusion criteria and were distributed to potential participants on site. After participants expressed their interest in the study, they were screened for eligibility to participate and their informed consent was secured. Next, a one-on-one interview was scheduled and a questionnaire was completed by participants to obtain their demographic data (gender, age, residential area, smoking status, etc.). One-on-one interviews were conducted in Mandarin, digitally recorded, with study data stored on a passworded encrypted laptop. Each interview lasted approximately 20 to 40 min. A private room in the clinic was used for all the in-depth interviews.

The interview questions were formulated based on the ODSF and after a comprehensive literature review [ 28 ], with extensive discussions among researchers of the study (Feifei Huang, PhD, RN, Professor, specializing in lung cancer prevention and psycho-oncology; Weisheng Chen, MD, specializing in lung cancer prevention, diagnosis and treatment; and Wei-Ti Chen PhD, RN, CNM, FAAN, specializing in intervention design and qualitative data collection). To ensure the acceptability and credibility of the interview guide, the interview questions were pilot tested with four participants in total, including two health care providers and two individuals at high-risk of lung cancer. As a result, some misconceptions regarding the interview questions were identified and subsequently modified. For instance, we replaced the term “decision tools” with “patient decision aids” to help participants to better understand the posed questions. The final interview questions are outlined in Table 1 . Tables 2 and 3 summarize key demographic data collected on the high-risk individuals and health care providers, respectively.

The sample size was determined by data saturation, that is, recruitment ended at the point where no new themes emerged from the participants’ experiences [ 29 ]. Data saturation was reached at approximately the twenty-seventh in-depth interview with a high-risk lung cancer individual, with another three high-risk lung cancer individuals being interviewed to ensure that the data reached complete saturation. Data saturation was reached at approximately the seventh in-depth interview with healthcare providers, with another two healthcare providers interviewed to ensure data saturation.

Data analysis

Since the interviews were conducted in Mandarin, a bilingual coding technique was used to keep the data in the original Chinese format, and the coding assignments were in English (e.g., decision negotiation). To ensure accuracy and minimize potential translation errors, two bilingual researchers (Chinese and English) reviewed and confirmed the translations [ 30 ]. The process of data analysis began with data collection. To analyze the data, content analysis was guided by the ODSF and Nvivo software version 12 was used [ 31 ]. The classification of themes was performed both inductively (derived from the quotes of research participants) and deductively (derived from the ODSF theoretical framework) under the principle of complementarity. The detailed steps of the data analysis process are illustrated in Fig. 1 .

Directed content analysis flowchart

Trustworthiness

Credibility, dependability, confirmability and transferability were employed to assure the trustworthiness of this study’s findings [ 32 ]. To enhance credibility, the researcher dedicated ample time to establishing meaningful interactions with the participants, thereby building trust for effective data collection. Regarding dependability, two researchers cross-checked and rectified codes that did not precisely reflect participants’ perspectives. Furthermore, an audit trail and reflexivity techniques were used during the data analysis process, which included tracking the interview and data analysis notes and memos. To ensure confirmability, the supervisor reviewed and selected quotations, codes, and categories, thereby validating the accuracy of the coding process. In terms of transferability, participants were purposefully selected from both urban and rural areas to incorporate a wide range of perspectives. Herein, a comprehensive description of the entire research process is presented to facilitate reproducibility of the study.

Out of a total of 44 participants consented, five participants (4 high-risk individuals and 1 health care provider) dropped out of the study due to their busy schedules and lack of interest in participating. A total of 39 eligible volunteers composed the study sample. Among them, 30 individuals were classified as at high-risk for lung cancer with an average age of 61.27 ± 7.92 years, while nine health care providers had an average age of 36.78 ± 7.45 years. Five health care provider participants specialized in lung cancer prevention, diagnosis, and treatment, and four specialized in general medical education and community cancer screening education. Detailed demographic information on the participants can be found in Tables  2 and 3 .

A total of 546 unique codes related to LCS shared decision-making were identified. Following the framework of the ODSF, participants’ decisional needs and supports for shared decision-making were categorized (refer to Fig.  2 ; Table  4 ).

Participants’ viewpoints on shared decision-making based on ODSF

Decisional needs

We identified four categories related to the theme of decisional needs, including LCS knowledge deficits, inadequate supportive resources, shared decision-making conceptual bias, and delicate doctor-patient bonds.

Theme 1: LCS knowledge deficit

Many high-risk study participants expressed that they did not have access to reliable and authoritative medical information. Many of the high-risk participants shared their inability to access LCS-related information and their limited capacity to distinguish accurate LCS information from misinformation. Furthermore, participants mentioned that a negative personal view of life influenced their active engagement in shared decision-making with health care providers and/or family, which diminished their comprehensive understanding of LCS.

“Some people are negative, they believe God’s will can decide everything, so when they faced a decision, they will ask the gods instead of making a decision according to their actual situation” H13 (high-risk individual, female, 53 years-old).

Theme 2: inadequate supportive resources

Participants emphasized that shared decision-making was hindered by financial, transportation and time-related barriers to hospital visits. Furthermore, unfamiliarity with the process of seeking medical treatment also presented an obstacle to shared decision-making. Notably, participants expressed negative emotions related to the LDCT test which influenced their shared decision-making. In particular, the LDCT process was not well received by individuals who had claustrophobia. Participants described feeling claustrophobic during the process of the imagological examination. The requirement for patients to lie flat during the examination, combined with the confined and dim space, can lead to feelings of depression and suffocation. Additionally, the machine’s noise and concerns about potential risks (such as radiation and false positives) from having LDCT scans may have heightened patients’ negative emotions and fears.

“Since I smoke, I’m always scared of getting bad test results. If the results are bad, it’s just really scary, I don’t think I have the sanity to make shared decisions with my doctors. I need help.” H11 (a high-risk individual, female, 54 years-old).

“I struggle with claustrophobia, and every time I have a test, I feel really trapped. It would be difficult for me to have shared decision-making when I have a claustrophobia. It felt like my mind was blank.” H12 (a high-risk individual, male, 52 years-old).

Several participants mentioned experiencing anxiety regarding the test results. They expressed their apprehension about potential adverse outcomes and indicated that this anxiety affected their ability to engage in shared decision-making with their doctors. Moreover, after experiencing claustrophobia, some participants expressed that they felt an inability to make shared decisions with their doctors in a rational manner.

Theme 3: Shared decision-making conceptual bias

Some participants mentioned that they were not familiar with the specific term ‘shared decision-making’. Health care providers shared the perspective that excessive communication with the high-risk group about their condition might lead to a refusal of subsequent treatment, potentially jeopardizing their health.

“I believe that when it comes to professional matters, it’s best to rely on trained professionals. Most patients don’t have expert medical knowledge, and even if they do, they might be hesitant about certain exams. That, in my opinion, doesn’t do much good for their health.” M8 (a general practitioner, female, 36 years-old).

Additionally, participants had misconceptions about shared decision-making. For example, health care providers had misconceptions about shared decision-making in LDCT screenings – some believed that shared decision-making meant merely providing information about the benefits and risks of LDCT; others confused the concepts of informed consent and shared decision-making all together; and a few providers viewed encouraging high-risk groups to conduct LDCT screening to be a part of shared decision-making. Some participants believed shared decision-making to be merely a procedural step to schedule a test appointment.

“I think shared decision-making means thoroughly informing those in high-risk groups about the pros and cons of a particular exam and ultimately letting them make the call.” M5 (a physician specialist, male, 25 years-old).

“When we suggest undergoing a medical examination, doctors might assume that this visit is a necessary step for patients to get a chance to be examined, not a step for shared decision-making. As a result, they may believe that there’s no necessity for patient education.” H13 (a high-risk individual, female, 53 years-old).

Theme 4: delicate doctor-patient bonds

Both health care providers and high-risk individuals emphasized that time constraints pose a significant barrier to shared decision-making. Some participants noted that doctors, who often express concerns about work-related burnout, were hesitant to provide comprehensive information about LDCT.

“I believe that doctor burnout contributes to their reluctance to discuss lung cancer screening with patients.” H9 (a high-risk individual, male, 57 years-old).

Furthermore, health care providers and participants encountered challenges with communication. Health care providers struggled to simplify complex information for easy understanding, while participants had difficulty clearly expressing their needs.

“Effective communication is essential for both doctors and patients. The doctor’s ability to convey information and the patient’s capacity to express their needs are crucial. Insufficient communication skills represent a challenge for both parties.” M6 (a physician specialist, male, 27 years-old).

Participants also mentioned that they were hesitant to express their thoughts to doctors whom they do not know well.

“Building trust is not a simple task. When patients and I have a strong connection and they trust us enough to share their true thoughts, it significantly reduces barriers to shared decision-making. On the other hand, some doctors who aren’t deeply connected with the community may struggle to gain patients’ trust, leading to communication challenges that hinder shared decision-making.” M2 (a nurse in grade A tertiary hospital, female, 41 years-old).

Others believe that the professional competence of doctors plays a pivotal role in shared decision-making in LCS. People often opt for doctors from tertiary hospitals who were perceived to have a higher level of professionalism, which is conducive to shared decision-making.

“Personally, I believe that the expertise of doctors in county-level hospitals may not be as advanced, which affects my level of trust in them. I tend to find doctors in top-tier tertiary hospitals to be more credible.” H12 (a high-risk individual, male, 52 years-old).

Decision support

Three categories related to the theme of decision support were identified: provide information throughout the LCS process, providing a shared decision-making coach, and provide decision tools.

Theme 1: provide information throughout the LCS process

Participants shared that they would like to know information about LDCT before and after undergoing the screening test. Desired information prior to screening included: eligibility criteria for LCS; benefits and risks of LDCT, the LDCT process itself, primary and secondary prevention of lung cancer, the cost of LDCT, potential emergencies and appropriate responses during LDCT, guidelines for Medicare reimbursement related to LDCT, and the medical visit steps. Most participants wanted information after the screening to include the interpretation and monitoring of LDCT results as well as the recommended frequency of LDCT.

Theme 2: providing a shared decision-making decision coach

Several participants said that it is necessary to enhance shared decision-making beliefs to better support the decision-making process for LCS, which is inherently a preference-sensitive decision.

“In China, shared decision-making isn’t commonly practiced. Many physicians here may not be familiar with the concept, even though it’s something they should consider adopting. Personally, I strongly believe in the importance of implementing shared decision-making.” H6 (a high-risk individual, male, 58 years-old).

High-risk individuals emphasize the importance of establishing a foundation for knowledge before engaging in shared decision-making. Participants advocated for a basic understanding of medical concepts, with decision counselors possessing specialized medical expertise.

“Before participating in shared decision-making, I’d like to gain some basic medical knowledge.” H4 (a high-risk individual, female, 53 years-old).

Due to time and energy constraints, clinicians found it challenging to engage in shared decision-making. However, the community doctors in our study stated that they had more time to communicate and share opinions and that their closer patient-provider relationships could facilitate the shared decision-making process in China.

“We only present the benefit and harm of LDCT briefly. We don’t have enough time to describe these in more detail. You know, lung cancer pathology and knowledge of imaging are too complex for high-risk individuals of lung cancer. For individuals who don’t have professional backgrounds, it is impossible for them to understand totally, what we can do is try to get them to understand as much as possible in a limited time.” M5 (a doctor in grade A tertiary hospital, male, 25 years-old).

“It’s important to involve community health providers in shared decision-making for a couple of reasons. Firstly, we tend to establish a strong rapport with patients, and they often trust us more compared to clinicians. Additionally, we have the advantage of spending more time communicating with patients, which makes us better suited to facilitate shared decision-making.” M9 (a general practitioner, male, 42 years-old).

Theme 3: providing decision tools

Participants expressed the need for decision tools and made several suggestions for decision tools to better cater to diverse groups. Decision tools are instruments that aid users in clarifying the congruence between their decisions and their individual values by presenting relevant options along with their associated benefits and potential drawbacks. Through the use of decision tools, users are assisted in arriving at clear, high-quality decisions.

The participants had several suggestions for providing decision tools. First, various information modalities such as videos, images, and written content should be integrated into tools to accommodate varying education levels and preferences. Second, tailored information that aligns with LCS decision-making is preferred. Third, a three-way interaction model involving patients, decision tools, and health care providers could enhance effectiveness. Fourth, medical knowledge should be presented in a comprehensible manner to improve accessibility. Additionally, access to more detailed information is necessary. Fifth, the time spent using decision tools should be less than 20 min to prevent impatience. Sixth, most participants emphasized addressing credibility concerns, through incorporating medical professionals into the tool’s development team, emphasizing authoritative sources, and involving experts from reputable hospitals. Finally, most participants acknowledged that value clarification exercises should be integrated to help users articulate their personal screening preferences to ensure a comprehensive approach to decision support.

Shared decision-making plays a crucial role in enhancing the understanding of LCS and LDCT in high-risk groups. Shared decision-making can also establish realistic expectations for health outcomes and ultimately improve decision-making for the best treatment or screening option [ 33 ]. This qualitative study provides insights into the decisional needs and necessary support for shared decision-making in LDCT screening, from the perspectives of health care providers and high-risk individuals in China. Specifically, LDCT screening decisions should evaluate the knowledge, availability of supportive resources, health care providers’ understanding of shared decision-making concepts, and quality of doctor-patient relationships. At present, both providers and screeners require decision support surrounding LDCT information and need shared decision-making coaching to effectively arrive at a decision. This study finding is valuable for shaping the design of future interventions that aim to facilitate decision-making and has the potential to increase the use of LDCT screening in Chinese society.

Our findings also contribute to the classification refinement of the ODSF. Regarding LCS knowledge, we have observed that high-risk groups not only lack specific knowledge of LCS, but also face challenges accessing relevant information and struggle with their capacity to distinguish accurate LCS information from misinformation. Previous multimodel public health interventions have focused on education related to specific LCS knowledge and ignored the need to access correct information, insufficiently addressing the needs of populations at high-risk of lung cancer [ 34 ]. Therefore, in addition to limited knowledge, limited access to information and lack of identification undermine the contributions of high-risk groups in shared decision-making.

In terms of support and resources, it is essential to consider not only conventional limitations such as financial and health system resources, but also the psychological well-being of high-risk populations. The proportion of smokers is greater among those at high-risk for lung cancer than among those at high-risk for other types of cancers (such as breast cancer and colorectal cancer) [ 35 ]. Being a smoker can affect the execution of shared decision-making due to perceived stigma, lung cancer fatalism, and heightened levels of worry and fear of contracting lung cancer [ 35 ]. Additionally, concerns about potential risks associated with LDCT serve as a barrier to the shared decision-making process with health care providers [ 9 ].

Our findings provide new insights into the core constructs of decisional needs, including awareness of shared decision-making and doctor-patient bonds. Additionally, shared decision-making awareness studies have demonstrated that bias can lead to differences in individual preferences, which can hinder the initiation of shared decision-making and result in higher levels of decision conflict [ 36 ]. Additionally, studies have shown that poor doctor-patient communication can lead to low-quality shared decision-making. For example, dismissive clinicians who dominate decision-making encounters, use negative verbal or nonverbal cues, or fail to respect patients’ concerns have been shown to act as barriers to shared decision-making for many patients [ 37 ]. Conversely, clinicians who strive to understand individual needs and preferences can foster a sense of partnership and facilitate their involvement in shared decision-making processes [ 38 ]. It has also been found that allocating limited time for consultations as well as poor communication skills results in ineffective shared decision-making [ 39 ]. Limitations in skill and time can impede the ability to be fully informed by health care providers, to process and reflect on the information received, and to engage in meaningful discussions between providers and individuals [ 37 ]. Furthermore, the presence of trust is identified as a facilitator of shared decision-making. Establishing a trusting relationship with health care providers encourages patients to feel more comfortable asking questions, sharing personal information, and discussing their concerns [ 39 ].

Currently, the use of shared decision-making in clinical practice is suboptimal in China [ 11 ]. Fortunately, our study provides potential mitigation strategies. First, the need for comprehensive decision tools that appeal to diverse groups of patients was emphasized by both high-risk groups and health providers. A decision tool can furnish information, facilitate patient-doctor dialog, and enhance therapeutic outcomes [ 33 ]. However, the availability of decision tools for LCS is limited and their applications are less than ideal, partly due to their failure to be tailored to personal needs. For instance, most LCS decision tools are presented as single-page materials or premade videos, which may not fully address participants’ needs. Our findings highlight the demand for personalized decision tools for LCS in China. Second, some participants suggested that decision counselors should not be limited solely to clinicians; community health care providers can also serve as counselors for decision-making. This aligns with the concept that shared decision-making requires multisectoral collaboration [ 40 ]. Community nurses in particular, share similar ethnic, linguistic, and geographic backgrounds with the residents they serve compared to other nurses. Consequently, they are more likely to encounter high-risk populations in the community [ 41 ]. Additionally, due to the nature of their work, they have more time to engage in shared decision-making discussions with high-risk groups. Research has revealed that community nurses, in their roles as coordinators, educators, researchers, navigators, and practitioners, can play multidimensional roles essential for leading successful LCS [ 42 ]. Hence, future research should actively promote the development of community nurses as counsellors for LCS to alleviate the burden on hospital-based physicians. Third, both health care providers and high-risk groups should receive education on shared decision-making. Our findings reveal that both sides still possess a vague understanding of shared decision-making, often conflating it with informed consent (patient-led) and paternalism (physician-led) models. Unlike in Western countries, humanistic medicine education in China is lacking, resulting in an inadequate grasp of patient-centered medical-ethical principles among health providers and patients [ 21 ]. Future interventions in China should emphasize humanistic medicine to establish the foundation of shared decision-making.

Our findings are rooted in Chinese culture, which, along with broader Asian cultural influences, places a significant emphasis on Confucianism and sociocultural values such as family support, care, and respect for familial hierarchy and authority [ 43 ]. Therefore, the insights provided by this paper may be applicable to other Asian countries. Despite the rapid development of SDM research in the West, the actual implementation of SDM in clinical practice is not as favorable [ 44 ]. One contributing factor is that highly developed patient decision aids often overly focus on standardized processes, deviating from a more humanistic approach that can be applied universally [ 44 ]. Moreover, the ongoing wave of globalization has resulted in increasingly multicultural societies, necessitating a broader scope of SDM coverage that includes individuals from diverse cultural backgrounds. Therefore, avoiding cultural stereotypes and actively inquiring about patients’ preferences become especially crucial. The results of our study contribute valuable insights into individual decisional needs and decision support from the perspectives of both individuals at high-risk for lung cancer and health care providers. These perspectives can assist patient decision aids in avoiding excessive standardization. Simultaneously, the perspective embedded in our findings is well-suited to accommodate the multicultural nature of Western countries. Future studies should seek to bridge the gap in SDM between Eastern and Western contexts.

Limitations

There are several limitations in this study. First, since the high-risk lung cancer individuals in our study did not undergo LCS shared decision-making recently, their views on LCS shared decision-making may have been subject to recall bias. Second, all study participants were from Fujian Province, which is a southeastern province in China. It is possible that recruitment from a broader geographical area may have led to a wider range of perspectives and experiences and thus influenced the point at which data saturation was reached. Third, as a qualitative, in-depth interview study, generalizations of findings to a larger population are not possible. Future quantitative studies should explore decision-making experiences among a broad range of high-risk groups and health care providers in China to enhance data triangulation and thus, the credibility and reliability of the study’s findings.

Guiding high-risk groups toward well-informed choices regarding LCS represents a substantial gain toward advancing secondary prevention of lung cancer. This descriptive qualitative study offers valuable insights into decision-making regarding LDCT screening among Chinese high-risk groups and their health care providers. The findings from this study highlight the decisional needs and decision support for shared decision-making for LCS using the ODSF conceptual framework. Future studies should target intervention development to offer decision support by evaluating individuals’ decisional needs, enabling them to make choices confidently, and with minimal conflict and decisional regret. In addition, this study may also serve as a starting point for the development of more effective decision tools for LDCT screening.

Availability of data and materials

The de-identified datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

Low-dose computed tomography

Lung cancer screening

The Ottawa Decision-Support Framework

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Acknowledgements

The authors are grateful to all the participants in this study.

This work was supported by the National Natural Science Foundation of China [grant number 72304068] and the General Project of Fujian Provincial Nature Science Foundation (grant number 2021J01133126).

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XJL had full access to all of the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. FFW and YLL contributed to the study design, data collection, data analysis and interpretation, and writing of the manuscript. FL. and WSC contributed to the recruitment, data collection and interpretation, and writing of the manuscript. WTC contributed to the study design, coordination, interpretation, and writing of the manuscript. FFH contributed to the overall study design, interpretation, and writing of the manuscript. All authors approved the final version of the manuscript.

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Lin, X., Wang, F., Li, Y. et al. Exploring shared decision-making needs in lung cancer screening among high-risk groups and health care providers in China: a qualitative study. BMC Cancer 24 , 613 (2024). https://doi.org/10.1186/s12885-024-12360-0

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DOI : https://doi.org/10.1186/s12885-024-12360-0

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• Lung neoplasms
• Qualitative research
• Shared decision-making

ISSN: 1471-2407

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