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How to write a logical framework (logframe)

Download logframe template and example  

Love them or hate them, logical frameworks (logframes) have become a staple of international development programs. Most donors will require one as part of your proposal, and it’s the first things an evaluator will ask for.

Our  logframe template is one of the most popular downloads on  tools4dev . This article provides more detailed steps on exactly how to use the template.

The purpose of a logframe

A logframe is a table that lists your program activities, short term outputs, medium term outcomes, and long term goal. It is supposed to show the logic of how the activities will lead to the outputs, which in tern lead to the outcomes, and ultimately the goal.

A logframe is different to a theory of change. For more information on the differences see our blog post  “Theory of Change vs Logical Framework – what’s the difference?”

Should I start with the goal or the activities?

Many people wonder whether they should start filling the logframe from the top (starting with the goal), or from the bottom (starting with the activities).

Some people would say to start from the top (goal) and work down. That way you state what you hope to achieve, and then you work backwards to decide which outcomes, outputs and activities are required to achieve it.

However, this is often difficult to do in practice. Many organisations have a fixed set of activities that requires them to go from the bottom up. Other organisations may go back and forth between the goal and the activities trying to balance costs against results.

So just fill the table in whichever order makes the most sense to you. For each row of the table (activities, outputs, outcomes and goal) you will need to complete t following steps.

Describe the project summary

The first column in the table is the project summary. It describes each level of your project. For example, the goal of the project could be a “10% increase in the number of Grades 5-6 primary students continuing on to high school within 3 years.” The outcome leading to this could be “improve reading proficiency among children in Grades 5-6 by 20% within 3 years.”

When completing the project summary it is very important that the links between the different levels of the project are realistic and logical. Activities should logically lead to outputs, which should lead to realistic outcomes, and a sensible goal.

Below is an example of a logframe where the links between levels are not logical or realistic (click the image to view a larger version).

Here is a version that is more realistic and logical (click the image to view a larger version).

If you’re having difficulty creating logical and realistic connections between the different levels, you might want to try creating a problem tree or theory of change first.

Choose indicators and means of verification

Once you’ve described each level of the project you need to choose indicators that will allow you to measure if it has been achieved. For example, if the goal is a “10% increase in the number of Grades 5-6 primary students continuing on to high school within 3 years” then the indicator is “percentage of Grades 5-6 primary students continuing on to high school.”

You can have more than one indicator for each level, but it’s a good idea to keep the total number of indicators manageable. For each indicator you need to describe how it will be measured – this is called the means of verification.

Here’s a example of these two columns:

The indicators in your logframe should match the indicators in your M&E framework. For more information on selecting indicators see our article on how to write a monitoring and evaluation (M&E) framework .

Identify risks and assumptions

The final column in a logframe is the risks and assumptions. This column lists things that must be true in order for one level to lead to the next level.

An easy way to check whether your risks/assumptions make sense is to look at the activities row and follow this logic: IF these activities are undertaken AND the assumptions are true THEN these outputs will be produced (see example below, click the image to view a larger version):

Then do the same with the outputs: IF the outputs are created AND the assumptions are true THEN the outcome will be achieved. And then the same for the outcome: IF the outcome is achieved AND the assumptions are true THEN the goal will be achieved.

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Article • 12 min read

Logframes and the Logical Framework Approach

Planning robust, coherent, successful projects.

By the Mind Tools Content Team

In practice, even the best project managers can find it difficult to plan major projects without missing important activities, and without failing to spot all significant risks and issues.

What's more, once you're immersed in the detail of project planning, it's hard to keep sight of the big picture: what are you trying to achieve and why? What are the risks and assumptions? And how you can tell whether the project is a success once it's implemented?

The Logical Framework Approach is a useful technique for helping you do these things, thereby making your projects more robust and coherent – and more successful.

The Logical Framework Approach (LFA) was developed in the 1970s as a tool for strategic planning, [1] using the ideas of Management by Objectives . It's a tool of choice used by development agencies and in the international donor community. Large aid organizations throughout the world use the LFA for planning, approving, evaluating and monitoring their projects. [2] That said, this is a powerful and useful technique, and is one that richly deserves much wider application than in international development alone.

The Logical Framework Approach and the Logframe

The Logical Framework Approach elegantly weaves together top-down and bottom-up approaches to project management. It brings together the classical, top-down, "waterfall approach" for identifying the activities in a project, with a rigorous bottom-up checking process to make sure that these activity lists are comprehensive. It then reinforces this with a rigorous risks and assumptions analysis, which is again thoroughly checked. And it concludes by identifying the controls needed to monitor and manage the project through to successful conclusion.

It does this within the framework of the Logframe Matrix, shown in figure 1 below. This cross-references seven key areas of the project to ensure that the key questions are asked:

• Goal – what results do we expect?
• Purpose – why are we doing this?
• Outputs – what are the deliverables?
• Activities – what will we do to deliver the outputs?
• Indicators of Achievement – how will we know we've been successful?
• Means of Verification – how will we check our reported results?
• Risks and Assumptions – what assumptions underlie the structure of our project and what is the risk they will not prevail?

The answers to these questions are put into a Logical Framework Matrix (Logframe) and become the output of the Logical Framework Analysis exercise. The Logframe is a four by four matrix, shown below:

Figure 1: The Logframe Matrix

The process has significant value for any size of project. It helps identify the big picture and allows you to see how other items cascade down from it. As well, it helps flesh out the core assumptions that are used in the project development process.

Using a Logframe

Carry out the following steps in consultation with your stakeholders , after you've completed a thorough analysis of the situation. By involving stakeholders, you'll end up with a much more robust analysis of the project than you would on your own.

Step 1: Identifying Outputs and Activities (Project Summary, Column 1)

The first step is to brainstorm the outputs and activities required by the project, starting with the project goal. Do this in the Project Summary column (column 1) of the Logframe. Start by defining the Goal and Purpose of the project and, from these, identify the outputs and the activities required:

• Goal: What is the "to be" state of the project? What are you trying to achieve?
• Purpose: What good will you do by achieving the goal? Who are the beneficiaries? What is the underlying motivation for starting the project in the first place?
• Outputs: What specific things will be delivered as a result of this project? In order for the project to be considered a success, what changes must be made, and what will the result be?
• Activities: What will actually be done in order to deliver the intended outputs? The Logframe is not intended as an implementation guide, so this section is typically presented in bullet point form.

Don't underestimate the amount of time and work needed to complete this process properly! Manage people's expectations on this, and keep them focused on the task in hand. If people lose focus, you'll miss important activities, false assumptions, and risks.

Step 2: Verify the Vertical Logic

Next, we take a bottom-up approach to checking that this list of activities will deliver the desired results – after all, it's possible that activities have been missed, or that the actual results of these activities may not be the ones wanted. This checking process is an important part of making sure that your project plan is robust.

Column one shows a hierarchy of objectives, so it is important to check that actions identified deliver the results wanted. Check the logic in column one by using an if/then test as follows. Starting with your activities, ensure that:

• IF you complete the activity, THEN the outputs will occur. You want to make sure your activities and outputs are directly linked.
• IF your outputs are achieved, THEN the purpose of your project will be satisfied. Are the planned outputs closely tied to your purpose? Make sure the beneficiaries you identified in your purpose actually receive the beneficial outcome desired.
• IF your purpose is satisfied, THEN the goal of the project is achieved. Examine your purpose and goal to make sure that the purpose fully incorporates the intent within the goal.

If, in this step, you find that activities and outputs are missing or are wrong, add or adjust them appropriately. And bear in mind that if you identify issues with elements higher up in this hierarchy, you'll need to go back to Step 1 and identify appropriate outcomes and activities for those elements.

Step 3: Identify the Risks and Assumptions of Your Plan (Column 4)

We now cross over to the other side of the Logframe to identify risks associated with the project, and possible false assumptions that may undermine it.

There are any number of external factors that can throw projects off course. In the planning and design phase, it is prudent to identify the major assumptions you've used and the degree of risk associated with them.

For each of the points in the project's structure (Column 1), identify the assumptions you're making (which may or may not be correct), and look at the associated risks.

To define your assumptions, ask "What actions or variables must exist for the project to start and proceed as planned?" Start at the bottom and work up.

• Activity Assumptions: What do you need to happen for your activities to be completed successfully? And what conditions and resources are you assuming will be in place?
• Output Assumptions: What factors outside of your control must be present to achieve the outputs you need?
• Purpose Assumptions: To achieve the purpose, what external factors do you need to have in place?
• Goal Assumptions: What are the necessary conditions for long-term viability of the project goal?

Clarify these assumptions with stakeholders immediately, if you can. If you can't, make sure you have early activities in place within your project plan to confirm that your assumptions are correct.

Next, repeat this process looking at risks (see our article on Risk Analysis .) Make sure you plan in all of the activities needed to manage or eliminate risk, and if risk can neither be managed or eliminated, make sure that it's clearly identified so that it can be evaluated in the next step.

Step 4: Verify the Logic of the Risks and Assumptions

Once you have identified assumptions and risks, you need to check them to determine:

• Whether your assumptions will link one level of the project to the next; and
• Whether risks are too large.

First of all, check that your assumptions are logical using an if/and/then analysis. Start at the bottom and work up to ensure:

• IF the activity is completed successfully, AND the assumptions underlying it are true, THEN the output will be delivered.
• IF the output is delivered, AND the assumptions underlying it are true, THEN the purpose will be achieved.
• IF the purpose is achieved, AND the assumptions underlying it are true, THEN the goal will be achieved.

Then, check some additional points related to your risk and assumption analysis:

• Make sure you have identified as many assumptions and risks as possible. Have you talked to everyone involved? Have you looked at the project from all angles?
• Make sure your assumptions are stated specifically and are not too vague. You can't assess risk accurately if you are working with generalities.
• Do you have plans at each level to manage the risks you have identified?
• If the risks you're not able to manage are too high, consider redesigning the project or, if you still can't reduce these to sensible levels, reconsider the project's viability.

Again, where this process exposes issues with your Logframe, update it appropriately.

Step 5: Determine the Indicators of Achievement and Means of Verification

When you are satisfied with the structure of the Logframe so far, and are comfortable that you can manage the risks related to your assumptions, you can move on to think about how you will monitor progress towards success.

Performance indicators are the specific measures used to monitor this progress. Here are the criteria for a good indicator of achievement:

• Valid – it must measure the intended result.
• Reliable – the measure must be consistently attained over time.
• Sensitive – the measure should respond to changes, and should sufficiently-quickly identify if things are going wrong.
• Simple – the measure should be easy to collect or perform.
• Useful – it must help with decision making or provide information for future learning.
• Affordable – you need to be able to afford the financial and time costs involved in taking the measurement on a regular basis.

Using these criteria, for each goal, purpose, output and activity, indicate what will be used to determine whether it was successfully achieved. Also note who will be responsible for setting these targets.

Then indicate exactly how you will verify that achievement. What sources of data will you use? How will you collect the data? How often?

Make sure that appropriate activities are in place within your plan to set up and manage these monitoring systems.

Click here for an example Logframe.

The Logical Framework Approach is a great technique for making sure that your project plan is robust and coherent. By using it, you significantly increase the likelihood that your project will be successful.

Firstly, it provides a useful framework for working through the design of your project with key stakeholders, making sure that you can take full advantage of their knowledge, insights and experience.

Secondly, it provides a useful process for testing and checking your project plan, making sure that it contains all the necessary activities, is based on sound assumptions, and fairly weighs and manages the risks inherent within the project.

Thirdly, it helps you ensure that appropriate control measures are embedded within the project, meaning that you can quickly identify where things are going wrong, and take appropriate corrective action.

[1] Practical Concepts Incorporated (PCI) (1979). The Logical Framework: A Manager's Guide to a Scientific Approach to Design & Evaluation . Available  here . [Accessed September 19, 2018.]

[2] Department for International Development (2003).  Tools for Development: a Handbook for Those Engaged in Development Activity . Available  here . [Accessed September 19, 2018.]

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What is a LogFrame?

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A Logframe is another name for Logical Framework, a planning tool consisting of a matrix which provides an overview of a project’s goal, activities and anticipated results.  It provides a structure to help specify the components of a project and its activities and for relating them to one another.  It also identifies the measures by which the project’s anticipated results will be monitored. 

The logical framework approach was developed in the late 1960s to assist the US Agency of International Development (USAID) with project planning.  Now most large international donor agencies use some type of logical or results framework to guide project design.

Logical Framework Structure

A Logical Framework (or LogFrame) consists of a matrix with four columns and four or more rows which summarize the key elements of the project plan including:

• The GOAL / OVERALL OBJECTIVE/ DEVELOPMENT OBJECTIVE
• The PURPOSE / IMMEDIATE OBJECTIVE
• The OUTPUTS
• The ACTIVITIES

In developing a logframe, it is very important to pay attention to how the objectives and results are formulated.  For reference, see Catholic Relief Services' (CRS) Guidance for Developing Logical and Results Frameworks .

The second and third columns summarize how the project’s achievements will be monitored and consists of the following:

• Indicators- a quantitative or qualitative measurement which provides a reliable way to measure changes connected to an intervention.  In essence “ a description of the project’s objectives in terms of quantity, quality, target group(s), time and place”
• Sources of verification - Describes the information sources necessary for data compilation that would allow the calculation of indicators.

Developing objectively verifiable indicators must also be a very careful process.  The USAID provides tips for selecting performance indicators . 

Lastly, the final column lists the following:

• Assumptions -the external factors or condition outside of the project’s direct control that are necessary to ensure the project’s success.

Example of the Logical Framework Structure and Intervention Logic

Logical Frameworks can look very different from one another depending on a donors requirements and the design team.  The terminology used also differs between donors.  See “ The Rosetta Stone of Logical Frameworks .”  Other similar tools include the Logic Model which is also an overall summary of a project plan and anticipated results or outcomes.  The following example is more in the format of a Logic Model:

Strengths of the Logical Framework Approach

• It draws together all key components of a planned activity into a clear set of statements to provide a convenient overview of a project.
• It sets up a framework for monitoring and evaluation where planned and actual results can be compared.
• It anticipates project implementation and helps plan out development activities.

Weaknesses of the Logical Framework Approach

• It may cause rigidity in program management.
• It is not a substitute for other technical, economic, social and environmental analyses.
• LogFrames are often developed after the activity has been designed rather than used as the basis for design.
• It can stifle innovative thinking and adaptive management.

Useful Resources :

• Project Cycle Management Guidelines  European Commission
• Handbook on Planning, Monitoring and Evaluating for Development Results UNDP (2009)(Exists in several languages including Russian)
• Tools for Development: A Handbook for Those Engaged in Development Activity  Department for International Development (DFID) UK (2003)
• USAID: Project Design Guidance (2011)
• The Logframe Handbook: A Logical Framework Approach to Project Cycle Management World Bank

References :

1 European Commission (2004). Project Cycle Management Guidelines. Accessed February 19, 2015 from: http://ec.europa.eu/europeaid/sites/devco/files/methodology-aid-delivery-methods-project-cycle-management-200403_en_2.pdf

2 From Ministry of Fisheries and Aquatic Resources (DFAR) Sri Lanka; Icelandic International Development Agency (ICEIDA); and United Nations University Fisheries Training Programme (UNU-FTP). World Bank (2005). The logframe Handbook; A Logical Framework Approach To Project Cycle Management. Accessed February 19, 2015 from: http://www-wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2005/06/07/000160016_20050607122225/Rendered/PDF/31240b0LFhandbook.pdf

About the Author

Kirsten Bording Collins is an experienced evaluation specialist providing consulting services in program evaluation, planning and project management.  She has over ten years of combined experience in the nonprofit, NGO and public sectors working both in the U.S. and internationally.  Kirsten's areas of expertise include: program evaluation, planning, project management, evaluation training and capacity-building, mixed-methods, qualitative analysis, and survey design.  Kirsten holds a MA in International Administration from the Korbel School of International Studies, University of Denver.  Kirsten grew up in Copenhagen, Denmark and currently lives in Washington, DC.

Connect with Kirsten on LinkedIn .

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• What is a logical framework?

The logical framework or logframe is a document that gives an overview of the objectives , activities and resources of a project. It also provides information about external elements that may influence the project, called assumptions . Finally, it tells you how the project will be monitored, through the use of /content/indicators . All this information is presented in a table with four columns and four rows – although variations on this basic scheme do exist.

The vertical logic

The first column of the 4x4 matrix shows the project logic (also called intervention logic) – hence the name logical framework. On the bottom row, you’ll find the project’s activities . When the activities are completed, we expect them to lead to tangible outputs . All the different results together will help to achieve the project’s purpose (sometimes called 'specific objective'). This is the main reason why the project was conceived in the first place. It is the problem that you want to resolve. In a broader context, the project’s purpose will help achieve one or more goals  (or 'general objectives'), which you can find in the top row. The term 'project logic' means that one thing leads to another:

• the activities lead to tangible outputs;
• the outputs lead to the project’s purpose;
• the purpose contributes to one or more goals.

But there is also the dimension of time. First we’ll do the activities, which fairly rapidly (in principle as soon as they’re finished) lead to outputs. The realisation of the purpose is further away, at the end or close to the end of the project. The effects or impact of the project is something that we’ll generally notice after a longer period.

The horizontal logic

This first column containing the project logic is about things that are under our control – more or less that is, especially as far as the goals or general objectives are concerned. Jumping directly to the last column, you’ll find the things that are not directly under the project’s control, but that may influence its realisation in a positive or a negative way. These are called assumptions , and they can be found in the fourth column. We speak of assumptions, because when we describe the project’s logic in the first column, we assume that everything goes well. But generally, this is but a mere dream, so we should take precautions to deal with these risks as best as we can. The relationship between the first and fourth column is as follows:

• When we do the activities, and our assumptions hold, we’ll achieve the expected outputs;
• When the outputs are achieved, and our assumptions hold, we’ll realise the project’s purpose;
• When the project’s purpose is attained, and our assumptions hold, we’ll contribute to the listed goals.

To make sure things go as planned and are not disrupted by the potential risks we identified (assumptions) or things we didn’t foresee, we have to set up a system to monitor progress and results. To do so, we use indicators , which can be found in the second column. An indicator is a piece of information we can use to get a (rather accurate) idea of how things are going (a process indicator) or what results have been achieved so far (result indicator).

For instance, if you want to see whether people are well nourished (or under nourished, or even over nourished), you may want to follow up their daily intake of calories. There is more to malnourishment than calorie intake, but it may give you a pretty good idea about how people are progressing. But you may also want to combine several indicators to get a completer picture, for instance to follow up the quality and diversity of the food that people eat.

There is much more to indicators than meets the eye; the art and science of monitoring is a whole field in itself – and is often a reason for much woe and sorrow when designing a logical framework.

Finally, the third column contains the verification sources . They describe where you can find the information of each indicator. Do you measure things yourself or do you ask someone else to do it for you? Or is the information readily available in reports or statistics from other sources?

The logical framework as a document is a tool that is used in many different approaches. It can be used to plan individual projects. It can also be used as a tool to plan, follow-up and evaluate more complex programs that consist of many different individual projects (or actions). And it can be a tool in a complete management approach for organisations. It can be used to plan, or to report, or as a part of a contract. Because of these different roles and different expectations by all the parties that are involved in the project, logframes sometimes have a tendency to become overly complex.

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Logical framework: Definition, elaboration and detailed example

The logical framework, also known as the logical framework methodology (LFM) or just Logframe, is a project management tool used in the design, planning, execution and evaluation of projects.

It was developed in 1969 by USAID (United States Agency for International Development) in response to analysis of the results of previous projects, where it was concluded that there were deficiencies and that it was necessary to create a tool to improve the results of future projects.

Subsequently, organizations and entities such as the Inter-American Development Bank (IDB), the German development cooperation agency (GTZ) and many governments in South America and the Caribbean adopted the logical framework approach (LFA).

Many of these adaptations generated variations of the methodology, such as the ZOPP methodology created by GTZ.

This indicates that the logical framework is mostly used in development projects at the state or governmental level. However, some private organizations and educational institutions have begun to adopt the methodology.

Table of Contents

The logical framework methodology is today’s topic in Ingenio Empresa. What is it, what is its importance and how is it done; is what I am going to tell you next.

Definition: What is the logical framework?

And to make it clearer, let’s look at some classic definitions:

Management tool that facilitates the planning, implementation and evaluation of a project. Norwegian Agency for Development Cooperation (NORAD), 1993.

A system of procedures and instruments for objective-oriented project planning. German Agency for Development Cooperation. (GTZ), 1987.

Analytical tool for objective-oriented project planning and management. It is a method with different steps from identification to formulation and its final result should be the elaboration of a project planning matrix. Spanish Agency for International Development Cooperation (AECID), 1999.

And now? This is our personal definition:

Logical Framework Methodology and Logical Framework Matrix What is the difference?

The difference is simple:  The result of the methodology is the matrix  .

That is, the methodology is composed of a series of steps such as problem analysis, analytical structure, project narrative, etc; as we follow the steps, we complete the logical framework matrix.

So what is the matrix for? It is a summary of the project. It sets out what the project is intended to do and how it is intended to be done, along with the assumptions it faces and how it will be monitored and evaluated. This is vital when, for example, you want to present the essence of the project to a potential investor.

How to do a project with the Logical Framework Methodology: Step-by-step example

Some follow the logical framework approach in 7 steps, others do it in 15. We are going to do it in 10 steps and the result will be a completely filled out matrix.

The “how to” of each step will be deepened in a separate post. That is, there will be a post for each step. There I will show you the activities and tasks for “that” step of the methodology.

While we could have approached the logical framework example with a social development project, as is commonly done with educational institutions, this time we will do it with a private company. Why?

I believe that for a methodology of this complexity and for those who have their first approach to it, handling a case study where it is not necessary to have knowledge of the issues addressed by the problem facilitates understanding.

So, I believe that this example will help you to have a solid foundation in the methodology. Of course, it is necessary to go a little deeper, so it will be necessary for you to review each post I reference here.

With that clear, we begin.

Example Case: Project with Logical Framework Methodology

Let me put you in context: Colusa Inc. is a company that provides web hosting services. For some time now (6 months to be more exact) it has been presenting a higher number of complaints and claims from its customers. This situation has impacted the company’s finances and threatens to further reduce its customer base.

In addition, Colusa Inc. has been left with an obsolete technological infrastructure, while its competitors (of which there are many) have upgraded their services or are in the process of doing so.

In other words, Colusa has realized that it is not keeping up with market trends and this is impacting service delivery. And if that were not enough, the company has been experiencing massive employee resignations.

With this in mind, they propose to address this problematic situation with a project under the logical framework methodology.

Step 1: Stakeholder analysis

In the stakeholder analysis we identify the stakeholder groups that are touched by the project, either directly or indirectly. Not only do we define who they are, but we also think about their interests, expectations and needs in order to define intervention strategies that allow us to have their support or to propose actions against their opposition.

The result of the stakeholder analysis for colusa is as follows:

The following strategies were defined:

How did we do it? Click here: Example of Stakeholder Analysis

Step 2: The problem tree

While we already know that there is a problem situation in Colusa, we have not yet characterized it. Much of what was mentioned above may be caused by a root problem, or what appears to be a problem may actually be a consequence of another, larger cause.

To get clarity on the problem situation, we will use the problem tree. With this tool, we will be able to represent the problem situation by locating its causes in the roots, the central conflict in the trunk and the effects in the leaves.

Transferring Colusa’s situation to the tree, the result is as follows:

The detailed step-by-step is available here: Example of problem tree .

Step 3: The objectives tree

In the also known solution tree, causes become means and leaves become ends. We move from a current negative state to a desired positive state, which means that the central problem of the project changes to the central purpose.

Following our case study, Colusa’s objective tree is:

You can find the how here: Example of objective tree .

Step 4: Analysis of alternatives

With the analysis of alternatives we stop dreaming and start taking action. How are we going to change the situation shown in the problem tree to what we want in the objective tree?

We do it with this analysis. In it, we identify the alternatives or set of means that can mean strategies to solve the problematic situation.

What we do in this analysis is to take the means of the objective tree and define actions that will allow me to reach that means. Subsequently we define and apply the criteria we believe relevant according to the nature of the problem (e.g. cost vs. benefit or social impact) to filter and leave only those optimal alternatives with which we will work in the following steps.

The strategies are the following and the way it was done is here: Example analysis of alternatives .

Staff training in computer, hosting and domain knowledge : Training programs will be carried out on new trends and technologies in these areas. Elaborate mystery shopping sessions with after-sales service personnel : Calls made from the company where the customer is acting as a customer to evaluate the quality of technical support. Implementation of support evaluation mechanisms at the end of the call and by e-mail sent to the customer : Implement telephone and e-mail surveys consisting of a question such as “rate the technical service from 1 to 5 with 1 being the lowest and 5 the highest”. Provide training to technical support staff : Customer service training. Negotiation with CMS providers to provide the customer with quick and easy installation : WordPress and Joomla will be negotiated. Implementation of Zapp : The book will be acquired and delivered to the company’s management.

Step 5: Analytical project structure

Next, in this step we take the work of the problem and objective tree together with the analysis of alternatives to outline the relationship of the strategy or optimal alternative with the objectives and actions .

In the analytical project structure or APS, we begin to build the hierarchical level relationship that is addressed in the logical framework matrix. The organization of the levels in the APSis as follows:

• First level: Activities
• Second level: Outputs
• Third level: Purpose
• Fourth level: Goal

And starting from the fourth level, the goal of the project is extracted from the top of the objective tree. The purpose is the central objective of the objective tree. The outputs are the result of having executed the strategies or alternatives of the alternatives analysis. Finally, for the first level we define the most relevant activities to execute the strategies and deliver the outputs.

The result of this exercise is as follows:

Any doubts? Check the step-by-step here: Example of project analytical structure .

Step 6: Project Narrative

It is in step 6 where we begin to create the logical framework matrix. The project narrative is the first column of step 6. It is nothing more than the arrangement of the levels of the analytical project structure on the logical framework matrix. In other words, each level of the structure is a row of the logframe matrix.

Goal, purpose, outputs and activities are the rows of the logical framework matrix.

• The goal is a medium- or long-term impact. It represents the contribution that is achieved by having the project completed. It may be composed of one or more elements.
• The purpose is the central objective of the project and there should be only one. The project is culminated once the goal is achieved, and it is achieved when the outputs are completed.
• The outputs or products are the deliverables (goods, services, tangible products) of the project. They are the result of having the activities completed.
• The activities are the “to do” necessary to deliver the project outputs.

To go deeper into this step, click here: Project Narrative Example .

The project narrative in our example is as follows:

Step 7: Objectively Verifiable Indicators

In the second column of the logframe matrix, we define how we will know the progress of the project , either current or final progress. We create indicators to measure everything in the project narrative.

What is important in this step? We can summarize it in two steps:

• All project stakeholders must be aware of them.
• They must be practical, independent and focused.
• There must be indicators to determine to what extent the objectives are achieved but also to monitor progress in the project timelines.
• More information: Example of indicators in the logical framework

To complete: Example of indicators in Logical Frame Matrix

With this step done, our logical framework matrix looks as follows:

Step 8: Means of verification

To know the status of the project, it is not enough to define the indicators; where and how are we going to obtain the data and information for measurement? The answer is part of the third column of the logframe matrix.

With the sources or means of verification we evaluate and monitor the indicators , defining the following aspects:

• Source of information
• Method of collection
• Responsible for collection
• Method of analysis

To go deeper: Example of means of verification in logical framework .

With the third column ready, this is our logical framework matrix:

Step 9: Assumptions

Assumptions are all those conditions or factors that we consider to be true but that are not controllable by the project team and that, in the event that they are not met, affect the results.

What can go wrong? Anything that can lead to an assumption not being met, consider it a risk. Therefore, in this step we try to ensure that the assumptions are met, so we identify everything that can go wrong before starting a project item or during its execution. By item I mean element of the project narrative (goal, purpose, outputs and activities).

This is how we did it in our case: Example of assumptions in logical framework .

And this is what we got, the complete logical framework matrix.

Step 10: Project Monitoring and Evaluation

Sometimes not everything goes as planned. It may happen that projects may have unforeseen events or delays or that things are not being done as they should be done. For this reason it is necessary to monitor and evaluate. In doing so, we seek to narrow the gap between actual and planned.

However, the two terms are different.

With monitoring we control the progress of the project and we do it only in the execution phase. Aspects such as costs, physical progress and compliance with deadlines are subject to verification. We must also inform those involved about the results of the monitoring and take the pertinent actions to ensure the continuity and success of the project.

Thus, in monitoring we identify to what extent:

• Activities are being performed on time and at the lowest cost – Efficiency .
• The outputs are being produced and whether the purpose is being achieved – Effectiveness

To take actions to correct the path of the project. That with respect to monitoring or follow-up.

Project evaluation means to put a point on the path and think:

Is the project working?

This simple question must be answered considering a vision of the project as a whole and not as something specific. For the specifics there is monitoring, in the evaluation we reflect on what has been done so far and the results obtained, which leads us to obtain high-level conclusions.

Is the project working, this question is asked throughout the project cycle (and beyond) but at defined moments. For example, in a project with an estimated duration of 3 years, we can make an annual evaluation while the project is ongoing. Once the project is completed and if applicable, we can do subsequent evaluations every 2 years to determine its impact. The result of an evaluation translates into very significant lessons learned for the ongoing project or similar and future projects.

Vertical Logic

The vertical logic is a sample of the validity of the project design . With it, we manage to analyze the causal links that exist between each of the levels of the column of objectives. In it, we consider that the project design or planning is valid when:

• The outputs are the result of having the activities done.
• Each activity is necessary to achieve the outputs. There is no need for activities and there are no extra activities.
• Once the outputs are obtained, the project is achieved.
• Once the achievement of the project is achieved, there is a contribution to the achievement of the goal.

Some software used in country development projects takes the project formulator step by step, but when an incongruence is detected in the vertical logic of the project, it does not allow him to advance any further and forces him to rethink his formulation.

Horizontal logic

We already explained this concept in the previous steps but without mentioning its real name, horizontal logic. It is probably not new to you if you have read the whole post.

The “objectives – indicators – means of verification”, so in that order, are called horizontal logic. The reason is the following:

• The means of verification must be sufficient to achieve the calculation of the indicators.
• The calculation of the indicators evidences the actual and final progress in the achievement of the objectives.

This and all the articles on the logframe methodology were based on the following sources:

A very complete work, both from the theoretical and practical aspects. They offer a methodological guideline where they show with examples everything exposed.

Ortegon, E., Pacheco, J. F., & Prieto, A. (2005). Metodología del marco lógico para la planificación, el seguimiento y la evaluación de proyectos y programas. Santiago de Chile: CEPAL. Retrieved November 20, 2016, from http://repositorio.cepal.org/bitstream/handle/11362/5607/S057518_es.pdf

Exposes in detail each of the phases of the logical framework, but its real value is in that it provides 10 examples of the application of the methodology in real problems.

Camacho, H., Camara, L., Cascante, R., Sainz, Héctor. (2001) El Enfoque del marco lógico: 10 casos prácticos. Madrid. Cideal. Retrieved September 05, 2017 from http://www.olacefs.com/wp-content/uploads/2014/07/DOC_27_8_2013_Enfoque_Marco_Logico_EML_10_casos.pdf

The following source exposes all the concepts in a graphic or schematic way, very useful if you want to take it as a reference to prepare your own explanation. It does not stop to explain everything as detailed as the previous one, instead it allows you to take immediate action on what to do when you approach a project through the logical framework approach.

Navaja Gómez, P. (n.d.). El enfoque de marco lógico. Retrieved November 20, 2016, from http://www.leganes.org/portal/RecursosWeb/DOCUMENTOS/1/0_32596_1.pdf

And if what you want is a summary that gives you an overview of the steps that make up the elaboration of the logical framework matrix, this one will be perfect for you.

Guía para formulación de proyectos bajo la metodología Marco Lógico. Technova. Retrieved September 05, 2017 from http://www.tecnnova.org/wp-content/uploads/2017/03/Cartilla-Resumen-Marco-L%C3%B3gico-para-Formulaci%C3%B3n-de-Proyectos-CEPAL-2011.pdf

The header image of the post is from: Freepik

23 thoughts on “Logical framework: Definition, elaboration and detailed example”

You welcome 🤗

interesting

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It is very easy to understand and helps as a guide for teaching. Thank you for Sharing.

I is very important issues to improve knowledge and skill.

very well presented, defined and easy to follow approach. Thanks alot

Thanks for comment!

This is well simplified to understand the Logical framework.

You welcome Stefano.

Thanks from Afghanistan.

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Thanks to you Obinna for commenting.

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Thank you very much for your comment Nabi. I am glad to have helped.

This is the most helpful resource I have come across .I like the way the topic is broken down in the simplest of all ways. Thank you for this amazing work. Shout outs from Uganda. The pearl of Africa

I’m very glad to hear that Sarah. Best regards

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The logical framework approach

• AusAid - The logical framework approach File type PDF File size 266.24 KB

This publication is part of a series of guidelines developed by AusAid in relation to activities design.

It provides an overview of the situation (the problem) and stakeholders involved and describes the logframe matrix with its activities. The document also gives details of the implementation of the logical framework approach.

• Introduction
• Analysing the situation
• The logframe matrix
• The LFA and different forms of aid
• Implementation, resource and cost schedules
• A Steps in conducting problem tree analysis
• B Stakeholder analysis tools
• C Reference numbers, flow charts & contractible outputs
• D Logical Framework Approach Terminology
• E Indicators and the link to M&E

AusAid. (2005). 3.3 The Logical Framework Approach. AusGuideline. Activity design: Commonwealth of Australia. Retrieved from http://www.sswm.info/sites/default/files/reference_attachments/AUSAID%202005%20The%20Logical%20Framework%20Approach.pdf

'The logical framework approach' is referenced in:

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Logical Framework

Please visit the updated Project Starter collection for the latest Trade Facilitation specific MEL content. For the latest MEL guidance in general please visit Learning Lab .

Central to the Project Design process is the Logical Framework (LogFrame), which is developed from the CDCS Results Framework. The Logical Frame validates and potentially updates the Result Framework and includes detail on the inputs and outputs necessary to achieve the intended results or project's purpose as well as project assumptions. The Logical Frame provides a way to define, design, and budget for the USAID interventions necessary to achieve the CDCS Goal and DOs.

The Logical Framework is as much a way of thinking about development projects as it is a one page tool for summarizing the key elements of a project design and establishing a basis for project monitoring and evaluation. Users already familiar with this tool may wish to jump directly to the kit's  Logical Framework Template  which can be used to prepare a project Logical Framework online and save, download or print it. Those less familiar with this tool will find introductory information on this page as well as highlighted terms that lead to more detailed explanations on how to prepare each of the columns of a Logical Framework matrix. A sample of a completed  Logical Framework for a Trade Facilitation Project  is also provided in this section.

Central to the way in which the Logical Framework approach focuses thinking about development projects is its emphasis on the hypotheses embedded in project designs. These hypotheses are expressed as a vertical chain or hierarchy, in the Narrative Summary column of the matrix shown below. As the arrows at the top of the matrix shown below indicate, Assumptions in the far right column of a Logical Framework matrix are an integral part of the design aspect of this tool. To understand a project's design propositions, readers are encouraged to read these two columns together, i.e., Outputs plus Assumptions, at the Output level, taken together will yield the project Purpose. Additional pages in this kit provide more detailed information on the preparation of a  Narrative Summary  and  Assumptions  columns in the matrix.

The vertical aspect of a Logical Framework is its scientific side. It reminds us that the development process is not fully understood, and in most environments factors beyond a USAID project's control introduce uncertainties into any design and implementation process. The scientific side encourages us to frame a vertical chain of results as hypotheses, which can be tested and from which we can learn and advance our understanding of “what works” to bring about progress in developing countries. Evaluations, particularly impact evaluations, focus on the vertical or scientific aspect of a Logical Framework.

The horizontal aspect of a Logical Framework, particularly the first three columns capture the managerial and accountability side of a Logical Framework. The second column,  Indicators , refines our understanding of results in the first column by telling us how we will know whether those results have been achieved. In a Logical Framework, targets as well as indicators are included in this column. The third column,  Data Sources , identifies data sources, methods and the frequency with which performance information will be obtained to help project managers guide implementation. Performance monitoring in a Logical Framework is part of the horizontal aspect of the tool, as are many of the questions USAID asks in mid-project performance evaluations. Additional pages in this kit provide more detailed information on the preparation of the  Indicators  and  Data Sources  columns in the matrix. Both performance and impact evaluations draw on the two middle columns for guidance on measuring the changes that projects are intended to bring about.

A Logical Framework for Understanding Why

• Conference paper
• First Online: 28 December 2023
• Cite this conference paper

• Yu Wei   ORCID: orcid.org/0000-0003-3530-6141 10  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14354))

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• European Summer School in Logic, Language and Information

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Epistemic logic pays barely any attention to the notion of understanding, which stands in total contrast to the current situation in epistemology and in philosophy of science. This paper studies understanding why in an epistemic-logic-style. It is generally acknowledged that understanding why moves beyond knowing why. Inspired by philosophical ideas, we consider whereas knowing why requires knowing horizontal explanations, understanding why additionally requires vertical explanations. Based on justification logic and existing logical work for knowing why, we build up a framework by introducing vertical explanations, and show it could accommodate different philosophical viewpoints via adding conditions to the models. A sound and complete axiomatization for the most general case is given.

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See, for instance [ 16 , 20 , 22 , 31 ] etc. All mentions of “understanding” in their work should be read as concerning “understanding why”. And the author of [ 20 ] refers to understanding why as a narrow conception of understanding.

For example, see [ 3 ] and the bibliographies therein.

As mentioned by an anonymous review, both the knowing why logic by [ 33 ] and the understanding why logic introduced in this paper could be seen as a sub-logic of some kind of justification logic with existential quantifiers and knowledge operators. Some work corresponding to the full logic have been formalized in the literature. For example, the authors of [ 6 ] propose an axiomatization of a justification logic with operators \(\exists r\varphi ,~B\varphi ,\) and \(r:\varphi \) to capture the notion of reason-based belief.

Note that the notions of knowing why and understanding why in Pritchard’s work are particularly to do with causal matters. The paper [ 19 ], to be mentioned later, also follows this restriction for simplicity.

In actual fact it is a question in justification logics for justification application as well, as remarked in [ 8 ].

For more details about these two notions the reader is referred to [ 26 ] and the bibliographies therein.

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Acknowledgements

The author wishes to thank Fernando Velazquez Quesada, Sonja Smets and Yanjing Wang for their patient guidance and helpful suggestions on this project. The research was also supported by CSC, which made my visit to ILLC possible. I’m grateful to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially. Many thanks to Qiang Wang for her inspired comments and careful inspection. Last but not least, the support from Shanghai Pujiang Program (Grant No. 22PJC034) is acknowledged.

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University of Trento, Rovereto, Italy

Raffaella Bernardi

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Wei, Y. (2024). A Logical Framework for Understanding Why. In: Pavlova, A., Pedersen, M.Y., Bernardi, R. (eds) Selected Reflections in Language, Logic, and Information. ESSLLI 2019. Lecture Notes in Computer Science, vol 14354. Springer, Cham. https://doi.org/10.1007/978-3-031-50628-4_13

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Home » Conceptual Framework – Types, Methodology and Examples

Conceptual Framework – Types, Methodology and Examples

Table of Contents

Conceptual Framework

Definition:

A conceptual framework is a structured approach to organizing and understanding complex ideas, theories, or concepts. It provides a systematic and coherent way of thinking about a problem or topic, and helps to guide research or analysis in a particular field.

A conceptual framework typically includes a set of assumptions, concepts, and propositions that form a theoretical framework for understanding a particular phenomenon. It can be used to develop hypotheses, guide empirical research, or provide a framework for evaluating and interpreting data.

Conceptual Framework in Research

In research, a conceptual framework is a theoretical structure that provides a framework for understanding a particular phenomenon or problem. It is a key component of any research project and helps to guide the research process from start to finish.

A conceptual framework provides a clear understanding of the variables, relationships, and assumptions that underpin a research study. It outlines the key concepts that the study is investigating and how they are related to each other. It also defines the scope of the study and sets out the research questions or hypotheses.

Types of Conceptual Framework

Types of Conceptual Framework are as follows:

Theoretical Framework

A theoretical framework is an overarching set of concepts, ideas, and assumptions that help to explain and interpret a phenomenon. It provides a theoretical perspective on the phenomenon being studied and helps researchers to identify the relationships between different concepts. For example, a theoretical framework for a study on the impact of social media on mental health might draw on theories of communication, social influence, and psychological well-being.

Conceptual Model

A conceptual model is a visual or written representation of a complex system or phenomenon. It helps to identify the main components of the system and the relationships between them. For example, a conceptual model for a study on the factors that influence employee turnover might include factors such as job satisfaction, salary, work-life balance, and job security, and the relationships between them.

Empirical Framework

An empirical framework is based on empirical data and helps to explain a particular phenomenon. It involves collecting data, analyzing it, and developing a framework to explain the results. For example, an empirical framework for a study on the impact of a new health intervention might involve collecting data on the intervention’s effectiveness, cost, and acceptability to patients.

Descriptive Framework

A descriptive framework is used to describe a particular phenomenon. It helps to identify the main characteristics of the phenomenon and to develop a vocabulary to describe it. For example, a descriptive framework for a study on different types of musical genres might include descriptions of the instruments used, the rhythms and beats, the vocal styles, and the cultural contexts of each genre.

Analytical Framework

An analytical framework is used to analyze a particular phenomenon. It involves breaking down the phenomenon into its constituent parts and analyzing them separately. This type of framework is often used in social science research. For example, an analytical framework for a study on the impact of race on police brutality might involve analyzing the historical and cultural factors that contribute to racial bias, the organizational factors that influence police behavior, and the psychological factors that influence individual officers’ behavior.

Conceptual Framework for Policy Analysis

A conceptual framework for policy analysis is used to guide the development of policies or programs. It helps policymakers to identify the key issues and to develop strategies to address them. For example, a conceptual framework for a policy analysis on climate change might involve identifying the key stakeholders, assessing their interests and concerns, and developing policy options to mitigate the impacts of climate change.

Logical Frameworks

Logical frameworks are used to plan and evaluate projects and programs. They provide a structured approach to identifying project goals, objectives, and outcomes, and help to ensure that all stakeholders are aligned and working towards the same objectives.

Conceptual Frameworks for Program Evaluation

These frameworks are used to evaluate the effectiveness of programs or interventions. They provide a structure for identifying program goals, objectives, and outcomes, and help to measure the impact of the program on its intended beneficiaries.

Conceptual Frameworks for Organizational Analysis

These frameworks are used to analyze and evaluate organizational structures, processes, and performance. They provide a structured approach to understanding the relationships between different departments, functions, and stakeholders within an organization.

Conceptual Frameworks for Strategic Planning

These frameworks are used to develop and implement strategic plans for organizations or businesses. They help to identify the key factors and stakeholders that will impact the success of the plan, and provide a structure for setting goals, developing strategies, and monitoring progress.

Components of Conceptual Framework

The components of a conceptual framework typically include:

• Research question or problem statement : This component defines the problem or question that the conceptual framework seeks to address. It sets the stage for the development of the framework and guides the selection of the relevant concepts and constructs.
• Concepts : These are the general ideas, principles, or categories that are used to describe and explain the phenomenon or problem under investigation. Concepts provide the building blocks of the framework and help to establish a common language for discussing the issue.
• Constructs : Constructs are the specific variables or concepts that are used to operationalize the general concepts. They are measurable or observable and serve as indicators of the underlying concept.
• Propositions or hypotheses : These are statements that describe the relationships between the concepts or constructs in the framework. They provide a basis for testing the validity of the framework and for generating new insights or theories.
• Assumptions : These are the underlying beliefs or values that shape the framework. They may be explicit or implicit and may influence the selection and interpretation of the concepts and constructs.
• Boundaries : These are the limits or scope of the framework. They define the focus of the investigation and help to clarify what is included and excluded from the analysis.
• Context : This component refers to the broader social, cultural, and historical factors that shape the phenomenon or problem under investigation. It helps to situate the framework within a larger theoretical or empirical context and to identify the relevant variables and factors that may affect the phenomenon.
• Relationships and connections: These are the connections and interrelationships between the different components of the conceptual framework. They describe how the concepts and constructs are linked and how they contribute to the overall understanding of the phenomenon or problem.
• Variables : These are the factors that are being measured or observed in the study. They are often operationalized as constructs and are used to test the propositions or hypotheses.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It includes the sampling strategy, data collection methods, data analysis techniques, and ethical considerations.
• Literature review : This component provides an overview of the existing research and theories related to the phenomenon or problem under investigation. It helps to identify the gaps in the literature and to situate the framework within the broader theoretical and empirical context.
• Outcomes and implications: These are the expected outcomes or implications of the study. They describe the potential contributions of the study to the theoretical and empirical knowledge in the field and the practical implications for policy and practice.

Conceptual Framework Methodology

Conceptual Framework Methodology is a research method that is commonly used in academic and scientific research to develop a theoretical framework for a study. It is a systematic approach that helps researchers to organize their thoughts and ideas, identify the variables that are relevant to their study, and establish the relationships between these variables.

Here are the steps involved in the conceptual framework methodology:

Identify the Research Problem

The first step is to identify the research problem or question that the study aims to answer. This involves identifying the gaps in the existing literature and determining what specific issue the study aims to address.

Conduct a Literature Review

The second step involves conducting a thorough literature review to identify the existing theories, models, and frameworks that are relevant to the research question. This will help the researcher to identify the key concepts and variables that need to be considered in the study.

Define key Concepts and Variables

The next step is to define the key concepts and variables that are relevant to the study. This involves clearly defining the terms used in the study, and identifying the factors that will be measured or observed in the study.

Develop a Theoretical Framework

Once the key concepts and variables have been identified, the researcher can develop a theoretical framework. This involves establishing the relationships between the key concepts and variables, and creating a visual representation of these relationships.

Test the Framework

The final step is to test the theoretical framework using empirical data. This involves collecting and analyzing data to determine whether the relationships between the key concepts and variables that were identified in the framework are accurate and valid.

Examples of Conceptual Framework

Some realtime Examples of Conceptual Framework are as follows:

• In economics , the concept of supply and demand is a well-known conceptual framework. It provides a structure for understanding how prices are set in a market, based on the interplay of the quantity of goods supplied by producers and the quantity of goods demanded by consumers.
• In psychology , the cognitive-behavioral framework is a widely used conceptual framework for understanding mental health and illness. It emphasizes the role of thoughts and behaviors in shaping emotions and the importance of cognitive restructuring and behavior change in treatment.
• In sociology , the social determinants of health framework provides a way of understanding how social and economic factors such as income, education, and race influence health outcomes. This framework is widely used in public health research and policy.
• In environmental science , the ecosystem services framework is a way of understanding the benefits that humans derive from natural ecosystems, such as clean air and water, pollination, and carbon storage. This framework is used to guide conservation and land-use decisions.
• In education, the constructivist framework is a way of understanding how learners construct knowledge through active engagement with their environment. This framework is used to guide instructional design and teaching strategies.

Applications of Conceptual Framework

Some of the applications of Conceptual Frameworks are as follows:

• Research : Conceptual frameworks are used in research to guide the design, implementation, and interpretation of studies. Researchers use conceptual frameworks to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data.
• Policy: Conceptual frameworks are used in policy-making to guide the development of policies and programs. Policymakers use conceptual frameworks to identify key factors that influence a particular problem or issue, and to develop strategies for addressing them.
• Education : Conceptual frameworks are used in education to guide the design and implementation of instructional strategies and curriculum. Educators use conceptual frameworks to identify learning objectives, select appropriate teaching methods, and assess student learning.
• Management : Conceptual frameworks are used in management to guide decision-making and strategy development. Managers use conceptual frameworks to understand the internal and external factors that influence their organizations, and to develop strategies for achieving their goals.
• Evaluation : Conceptual frameworks are used in evaluation to guide the development of evaluation plans and to interpret evaluation results. Evaluators use conceptual frameworks to identify key outcomes, indicators, and measures, and to develop a logic model for their evaluation.

Purpose of Conceptual Framework

The purpose of a conceptual framework is to provide a theoretical foundation for understanding and analyzing complex phenomena. Conceptual frameworks help to:

• Guide research : Conceptual frameworks provide a framework for researchers to develop hypotheses, identify research questions, and select appropriate methods for collecting and analyzing data. By providing a theoretical foundation for research, conceptual frameworks help to ensure that research is rigorous, systematic, and valid.
• Provide clarity: Conceptual frameworks help to provide clarity and structure to complex phenomena by identifying key concepts, relationships, and processes. By providing a clear and systematic understanding of a phenomenon, conceptual frameworks help to ensure that researchers, policymakers, and practitioners are all on the same page when it comes to understanding the issue at hand.
• Inform decision-making : Conceptual frameworks can be used to inform decision-making and strategy development by identifying key factors that influence a particular problem or issue. By understanding the complex interplay of factors that contribute to a particular issue, decision-makers can develop more effective strategies for addressing the problem.
• Facilitate communication : Conceptual frameworks provide a common language and conceptual framework for researchers, policymakers, and practitioners to communicate and collaborate on complex issues. By providing a shared understanding of a phenomenon, conceptual frameworks help to ensure that everyone is working towards the same goal.

When to use Conceptual Framework

There are several situations when it is appropriate to use a conceptual framework:

• To guide the research : A conceptual framework can be used to guide the research process by providing a clear roadmap for the research project. It can help researchers identify key variables and relationships, and develop hypotheses or research questions.
• To clarify concepts : A conceptual framework can be used to clarify and define key concepts and terms used in a research project. It can help ensure that all researchers are using the same language and have a shared understanding of the concepts being studied.
• To provide a theoretical basis: A conceptual framework can provide a theoretical basis for a research project by linking it to existing theories or conceptual models. This can help researchers build on previous research and contribute to the development of a field.
• To identify gaps in knowledge : A conceptual framework can help identify gaps in existing knowledge by highlighting areas that require further research or investigation.
• To communicate findings : A conceptual framework can be used to communicate research findings by providing a clear and concise summary of the key variables, relationships, and assumptions that underpin the research project.

Characteristics of Conceptual Framework

key characteristics of a conceptual framework are:

• Clear definition of key concepts : A conceptual framework should clearly define the key concepts and terms being used in a research project. This ensures that all researchers have a shared understanding of the concepts being studied.
• Identification of key variables: A conceptual framework should identify the key variables that are being studied and how they are related to each other. This helps to organize the research project and provides a clear focus for the study.
• Logical structure: A conceptual framework should have a logical structure that connects the key concepts and variables being studied. This helps to ensure that the research project is coherent and consistent.
• Based on existing theory : A conceptual framework should be based on existing theory or conceptual models. This helps to ensure that the research project is grounded in existing knowledge and builds on previous research.
• Testable hypotheses or research questions: A conceptual framework should include testable hypotheses or research questions that can be answered through empirical research. This helps to ensure that the research project is rigorous and scientifically valid.
• Flexibility : A conceptual framework should be flexible enough to allow for modifications as new information is gathered during the research process. This helps to ensure that the research project is responsive to new findings and is able to adapt to changing circumstances.

Advantages of Conceptual Framework

Advantages of the Conceptual Framework are as follows:

• Clarity : A conceptual framework provides clarity to researchers by outlining the key concepts and variables that are relevant to the research project. This clarity helps researchers to focus on the most important aspects of the research problem and develop a clear plan for investigating it.
• Direction : A conceptual framework provides direction to researchers by helping them to develop hypotheses or research questions that are grounded in existing theory or conceptual models. This direction ensures that the research project is relevant and contributes to the development of the field.
• Efficiency : A conceptual framework can increase efficiency in the research process by providing a structure for organizing ideas and data. This structure can help researchers to avoid redundancies and inconsistencies in their work, saving time and effort.
• Rigor : A conceptual framework can help to ensure the rigor of a research project by providing a theoretical basis for the investigation. This rigor is essential for ensuring that the research project is scientifically valid and produces meaningful results.
• Communication : A conceptual framework can facilitate communication between researchers by providing a shared language and understanding of the key concepts and variables being studied. This communication is essential for collaboration and the advancement of knowledge in the field.
• Generalization : A conceptual framework can help to generalize research findings beyond the specific study by providing a theoretical basis for the investigation. This generalization is essential for the development of knowledge in the field and for informing future research.

Limitations of Conceptual Framework

Limitations of Conceptual Framework are as follows:

• Limited applicability: Conceptual frameworks are often based on existing theory or conceptual models, which may not be applicable to all research problems or contexts. This can limit the usefulness of a conceptual framework in certain situations.
• Lack of empirical support : While a conceptual framework can provide a theoretical basis for a research project, it may not be supported by empirical evidence. This can limit the usefulness of a conceptual framework in guiding empirical research.
• Narrow focus: A conceptual framework can provide a clear focus for a research project, but it may also limit the scope of the investigation. This can make it difficult to address broader research questions or to consider alternative perspectives.
• Over-simplification: A conceptual framework can help to organize and structure research ideas, but it may also over-simplify complex phenomena. This can limit the depth of the investigation and the richness of the data collected.
• Inflexibility : A conceptual framework can provide a structure for organizing research ideas, but it may also be inflexible in the face of new data or unexpected findings. This can limit the ability of researchers to adapt their research project to new information or changing circumstances.
• Difficulty in development : Developing a conceptual framework can be a challenging and time-consuming process. It requires a thorough understanding of existing theory or conceptual models, and may require collaboration with other researchers.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 15 May 2024

Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke

• Helena Teede 1 , 2   na1 ,
• Dominique A. Cadilhac 3 , 4   na1 ,
• Tara Purvis 3 ,
• Monique F. Kilkenny 3 , 4 ,
• Bruce C.V. Campbell 4 , 5 , 6 ,
• Coralie English 7 ,
• Alison Johnson 2 ,
• Emily Callander 1 ,
• Rohan S. Grimley 8 , 9 ,
• Christopher Levi 10 ,
• Sandy Middleton 11 , 12 ,
• Kelvin Hill 13 &
• Joanne Enticott   ORCID: orcid.org/0000-0002-4480-5690 1  

BMC Medicine volume  22 , Article number:  198 ( 2024 ) Cite this article

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In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit.

Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement.

Conclusions

The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

Peer Review reports

Internationally, health systems are facing a crisis, driven by an ageing population, increasing complexity, multi-morbidity, rapidly advancing health technology and rising costs that threaten sustainability and mandate transformation and improvement [ 1 , 2 ]. Although research has generated solutions to healthcare challenges, and the advent of big data and digital health holds great promise, entrenched siloes and poor integration of knowledge generation, knowledge implementation and healthcare delivery between stakeholders, curtails momentum towards, and consistent attainment of, evidence-and value-based care [ 3 ]. This is compounded by the short supply of research and innovation leadership within the healthcare sector, and poorly integrated and often inaccessible health data systems, which have crippled the potential to deliver on digital-driven innovation [ 4 ]. Current approaches to healthcare improvement are also often isolated with limited sustainability, scale-up and impact [ 5 ].

Evidence suggests that integration and partnership across academic and healthcare delivery stakeholders are key to progress, including those with lived experience and their families (referred to here as consumers and community), diverse disciplines (both research and clinical), policy makers and funders. Utilization of evidence from research and evidence from practice including data from routine care, supported by implementation research, are key to sustainably embedding improvement and optimising health care and outcomes. A strategy to achieve this integration is through the Learning Health System (LHS) (Fig.  1 ) [ 2 , 6 , 7 , 8 ]. Although there are numerous publications on LHS approaches [ 9 , 10 , 11 , 12 ], many focus on research perspectives and data, most do not demonstrate tangible healthcare improvement or better health outcomes. [ 6 ]

Monash Learning Health System: The Learn Together for Better Health Framework developed by Monash Partners and Monash University (from Enticott et al. 2021 [ 7 ]). Four evidence quadrants: Q1 (orange) is evidence from stakeholders; Q2 (green) is evidence from research; Q3 (light blue) is evidence from data; and, Q4 (dark blue) is evidence from implementation and healthcare improvement

In developed nations, it has been estimated that 60% of care provided aligns with the evidence base, 30% is low value and 10% is potentially harmful [ 13 ]. In some areas, clinical advances have been rapid and research and evidence have paved the way for dramatic improvement in outcomes, mandating rapid implementation of evidence into healthcare (e.g. polio and COVID-19 vaccines). However, healthcare improvement is challenging and slow [ 5 ]. Health systems are highly complex in their design, networks and interacting components, and change is difficult to enact, sustain and scale up. [ 3 ] New effective strategies are needed to meet community needs and deliver evidence-based and value-based care, which reorients care from serving the provider, services and system, towards serving community needs, based on evidence and quality. It goes beyond cost to encompass patient and provider experience, quality care and outcomes, efficiency and sustainability [ 2 , 6 ].

The costs of stroke care are expected to rise rapidly in the next decades, unless improvements in stroke care to reduce the disabling effects of strokes can be successfully developed and implemented [ 14 ]. Here, we briefly describe the Monash LHS framework (Fig.  1 ) [ 2 , 6 , 7 ] and outline an exemplar case in order to demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare. The Australian LHS exemplar in stroke care has driven nationwide improvement in stroke care since 2007.

An evidence-based Learning Health System framework

In Australia, members of this author group (HT, AJ, JE) have rigorously co-developed an evidence-based LHS framework, known simply as the Monash LHS [ 7 ]. The Monash LHS was designed to support sustainable, iterative and continuous robust benefit of improved clinical outcomes. It was created with national engagement in order to be applicable to Australian settings. Through this rigorous approach, core LHS principles and components have been established (Fig.  1 ). Evidence shows that people/workforce, culture, standards, governance and resources were all key to an effective LHS [ 2 , 6 ]. Culture is vital including trust, transparency, partnership and co-design. Key processes include legally compliant data sharing, linkage and governance, resources, and infrastructure [ 4 ]. The Monash LHS integrates disparate and often siloed stakeholders, infrastructure and expertise to ‘Learn Together for Better Health’ [ 7 ] (Fig.  1 ). This integrates (i) evidence from community and stakeholders including priority areas and outcomes; (ii) evidence from research and guidelines; (iii) evidence from practice (from data) with advanced analytics and benchmarking; and (iv) evidence from implementation science and health economics. Importantly, it starts with the problem and priorities of key stakeholders including the community, health professionals and services and creates an iterative learning system to address these. The following case study was chosen as it is an exemplar of how a Monash LHS-aligned national stroke program has delivered clinical benefit.

Australian Stroke Learning Health System

Internationally, the application of LHS approaches in stroke has resulted in improved stroke care and outcomes [ 12 ]. For example, in Canada a sustained decrease in 30-day in-hospital mortality has been found commensurate with an increase in resources to establish the multifactorial stroke system intervention for stroke treatment and prevention [ 15 ]. Arguably, with rapid advances in evidence and in the context of an ageing population with high cost and care burden and substantive impacts on quality of life, stroke is an area with a need for rapid research translation into evidence-based and value-based healthcare improvement. However, a recent systematic review found that the existing literature had few comprehensive examples of LHS adoption [ 12 ]. Although healthcare improvement systems and approaches were described, less is known about patient-clinician and stakeholder engagement, governance and culture, or embedding of data informatics into everyday practice to inform and drive improvement [ 12 ]. For example, in a recent review of quality improvement collaborations, it was found that although clinical processes in stroke care are improved, their short-term nature means there is uncertainty about sustainability and impacts on patient outcomes [ 16 ]. Table  1 provides the main features of the Australian Stroke LHS based on the four core domains and eight elements of the Learning Together for Better Health Framework described in Fig.  1 . The features are further expanded on in the following sections.

Evidence from stakeholders (LHS quadrant 1, Fig.  1 )

Engagement, partners and priorities.

Within the stroke field, there have been various support mechanisms to facilitate an LHS approach including partnership and broad stakeholder engagement that includes clinical networks and policy makers from different jurisdictions. Since 2008, the Australian Stroke Coalition has been co-led by the Stroke Foundation, a charitable consumer advocacy organisation, and Stroke Society of Australasia a professional society with membership covering academics and multidisciplinary clinician networks, that are collectively working to improve stroke care ( https://australianstrokecoalition.org.au/ ). Surveys, focus groups and workshops have been used for identifying priorities from stakeholders. Recent agreed priorities have been to improve stroke care and strengthen the voice for stroke care at a national ( https://strokefoundation.org.au/ ) and international level ( https://www.world-stroke.org/news-and-blog/news/world-stroke-organization-tackle-gaps-in-access-to-quality-stroke-care ), as well as reduce duplication amongst stakeholders. This activity is built on a foundation and culture of research and innovation embedded within the stroke ‘community of practice’. Consumers, as people with lived experience of stroke are important members of the Australian Stroke Coalition, as well as representatives from different clinical colleges. Consumers also provide critical input to a range of LHS activities via the Stroke Foundation Consumer Council, Stroke Living Guidelines committees, and the Australian Stroke Clinical Registry (AuSCR) Steering Committee (described below).

Evidence from research (LHS quadrant 2, Fig.  1 )

Advancement of the evidence for stroke interventions and synthesis into clinical guidelines.

To implement best practice, it is crucial to distil the large volume of scientific and trial literature into actionable recommendations for clinicians to use in practice [ 24 ]. The first Australian clinical guidelines for acute stroke were produced in 2003 following the increasing evidence emerging for prevention interventions (e.g. carotid endarterectomy, blood pressure lowering), acute medical treatments (intravenous thrombolysis, aspirin within 48 h of ischemic stroke), and optimised hospital management (care in dedicated stroke units by a specialised and coordinated multidisciplinary team) [ 25 ]. Importantly, a number of the innovations were developed, researched and proven effective by key opinion leaders embedded in the Australian stroke care community. In 2005, the clinical guidelines for Stroke Rehabilitation and Recovery [ 26 ] were produced, with subsequent merged guidelines periodically updated. However, the traditional process of periodic guideline updates is challenging for end users when new research can render recommendations redundant and this lack of currency erodes stakeholder trust [ 27 ]. In response to this challenge the Stroke Foundation and Cochrane Australia entered a pioneering project to produce the first electronic ‘living’ guidelines globally [ 20 ]. Major shifts in the evidence for reperfusion therapies (e.g. extended time-window intravenous thrombolysis and endovascular clot retrieval), among other advances, were able to be converted into new recommendations, approved by the Australian National Health and Medical Research Council within a few months of publication. Feedback on this process confirmed the increased use and trust in the guidelines by clinicians. The process informed other living guidelines programs, including the successful COVID-19 clinical guidelines [ 28 ].

However, best practice clinical guideline recommendations are necessary but insufficient for healthcare improvement and nesting these within an LHS with stakeholder partnership, enables implementation via a range of proven methods, including audit and feedback strategies [ 29 ].

Evidence from data and practice (LHS quadrant 3, Fig.  1 )

Data systems and benchmarking : revealing the disparities in care between health services. A national system for standardized stroke data collection was established as the National Stroke Audit program in 2007 by the Stroke Foundation [ 30 ] following various state-level programs (e.g. New South Wales Audit) [ 31 ] to identify evidence-practice gaps and prioritise improvement efforts to increase access to stroke units and other acute treatments [ 32 ]. The Audit program alternates each year between acute (commencing in 2007) and rehabilitation in-patient services (commencing in 2008). The Audit program provides a ‘deep dive’ on the majority of recommendations in the clinical guidelines whereby participating hospitals provide audits of up to 40 consecutive patient medical records and respond to a survey about organizational resources to manage stroke. In 2009, the AuSCR was established to provide information on patients managed in acute hospitals based on a small subset of quality processes of care linked to benchmarked reports of performance (Fig.  2 ) [ 33 ]. In this way, the continuous collection of high-priority processes of stroke care could be regularly collected and reviewed to guide improvement to care [ 34 ]. Plus clinical quality registry programs within Australia have shown a meaningful return on investment attributed to enhanced survival, improvements in quality of life and avoided costs of treatment or hospital stay [ 35 ].

Example performance report from the Australian Stroke Clinical Registry: average door-to-needle time in providing intravenous thrombolysis by different hospitals in 2021 [ 36 ]. Each bar in the figure represents a single hospital

The Australian Stroke Coalition endorsed the creation of an integrated technological solution for collecting data through a single portal for multiple programs in 2013. In 2015, the Stroke Foundation, AuSCR consortium, and other relevant groups cooperated to design an integrated data management platform (the Australian Stroke Data Tool) to reduce duplication of effort for hospital staff in the collection of overlapping variables in the same patients [ 19 ]. Importantly, a national data dictionary then provided the common data definitions to facilitate standardized data capture. Another important feature of AuSCR is the collection of patient-reported outcome surveys between 90 and 180 days after stroke, and annual linkage with national death records to ascertain survival status [ 33 ]. To support a LHS approach, hospitals that participate in AuSCR have access to a range of real-time performance reports. In efforts to minimize the burden of data collection in the AuSCR, interoperability approaches to import data directly from hospital or state-level managed stroke databases have been established (Fig.  3 ); however, the application has been variable and 41% of hospitals still manually enter all their data.

Current status of automated data importing solutions in the Australian Stroke Clinical Registry, 2022, with ‘ n ’ representing the number of hospitals. AuSCR, Australian Stroke Clinical Registry; AuSDaT, Australian Stroke Data Tool; API, Application Programming Interface; ICD, International Classification of Diseases; RedCAP, Research Electronic Data Capture; eMR, electronic medical records

For acute stroke care, the Australian Commission on Quality and Safety in Health Care facilitated the co-design (clinicians, academics, consumers) and publication of the national Acute Stroke Clinical Care Standard in 2015 [ 17 ], and subsequent review [ 18 ]. The indicator set for the Acute Stroke Standard then informed the expansion of the minimum dataset for AuSCR so that hospitals could routinely track their performance. The national Audit program enabled hospitals not involved in the AuSCR to assess their performance every two years against the Acute Stroke Standard. Complementing these efforts, the Stroke Foundation, working with the sector, developed the Acute and Rehabilitation Stroke Services Frameworks to outline the principles, essential elements, models of care and staffing recommendations for stroke services ( https://informme.org.au/guidelines/national-stroke-services-frameworks ). The Frameworks are intended to guide where stroke services should be developed, and monitor their uptake with the organizational survey component of the Audit program.

Evidence from implementation and healthcare improvement (LHS quadrant 4, Fig.  1 )

Research to better utilize and augment data from registries through linkage [ 37 , 38 , 39 , 40 ] and to ensure presentation of hospital or service level data are understood by clinicians has ensured advancement in the field for the Australian Stroke LHS [ 41 ]. Importantly, greater insights into whole patient journeys, before and after a stroke, can now enable exploration of value-based care. The LHS and stroke data platform have enabled focused and time-limited projects to create a better understanding of the quality of care in acute or rehabilitation settings [ 22 , 42 , 43 ]. Within stroke, all the elements of an LHS culminate into the ready availability of benchmarked performance data and support for implementation of strategies to address gaps in care.

Implementation research to grow the evidence base for effective improvement interventions has also been a key pillar in the Australian context. These include multi-component implementation interventions to achieve behaviour change for particular aspects of stroke care, [ 22 , 23 , 44 , 45 ] and real-world approaches to augmenting access to hyperacute interventions in stroke through the use of technology and telehealth [ 46 , 47 , 48 , 49 ]. The evidence from these studies feeds into the living guidelines program and the data collection systems, such as the Audit program or AuSCR, which are then amended to ensure data aligns to recommended care. For example, the use of ‘hyperacute aspirin within the first 48 h of ischemic stroke’ was modified to be ‘hyperacute antiplatelet…’ to incorporate new evidence that other medications or combinations are appropriate to use. Additionally, new datasets have been developed to align with evidence such as the Fever, Sugar, and Swallow variables [ 42 ]. Evidence on improvements in access to best practice care from the acute Audit program [ 50 ] and AuSCR is emerging [ 36 ]. For example, between 2007 and 2017, the odds of receiving intravenous thrombolysis after ischemic stroke increased by 16% 9OR 1.06 95% CI 1.13–1.18) and being managed in a stroke unit by 18% (OR 1.18 95% CI 1.17–1.20). Over this period, the median length of hospital stay for all patients decreased from 6.3 days in 2007 to 5.0 days in 2017 [ 51 ]. When considering the number of additional patients who would receive treatment in 2017 in comparison to 2007 it was estimated that without this additional treatment, over 17,000 healthy years of life would be lost in 2017 (17,786 disability-adjusted life years) [ 51 ]. There is evidence on the cost-effectiveness of different system-focussed strategies to augment treatment access for acute ischemic stroke (e.g. Victorian Stroke Telemedicine program [ 52 ] and Melbourne Mobile Stroke Unit ambulance [ 53 ]). Reciprocally, evidence from the national Rehabilitation Audit, where the LHS approach has been less complete or embedded, has shown fewer areas of healthcare improvement over time [ 51 , 54 ].

Within the field of stroke in Australia, there is indirect evidence that the collective efforts that align to establishing the components of a LHS have had an impact. Overall, the age-standardised rate of stroke events has reduced by 27% between 2001 and 2020, from 169 to 124 events per 100,000 population. Substantial declines in mortality rates have been reported since 1980. Commensurate with national clinical guidelines being updated in 2007 and the first National Stroke Audit being undertaken in 2007, the mortality rates for men (37.4 deaths per 100,000) and women (36.1 deaths per 100,0000 has declined to 23.8 and 23.9 per 100,000, respectively in 2021 [ 55 ].

Underpinning the LHS with the integration of the four quadrants of evidence from stakeholders, research and guidelines, practice and implementation, and core LHS principles have been addressed. Leadership and governance have been important, and programs have been established to augment workforce training and capacity building in best practice professional development. Medical practitioners are able to undertake courses and mentoring through the Australasian Stroke Academy ( http://www.strokeacademy.com.au/ ) while nurses (and other health professionals) can access teaching modules in stroke care from the Acute Stroke Nurses Education Network ( https://asnen.org/ ). The Association of Neurovascular Clinicians offers distance-accessible education and certification to develop stroke expertise for interdisciplinary professionals, including advanced stroke co-ordinator certification ( www.anvc.org ). Consumer initiative interventions are also used in the design of the AuSCR Public Summary Annual reports (available at https://auscr.com.au/about/annual-reports/ ) and consumer-related resources related to the Living Guidelines ( https://enableme.org.au/resources ).

The important success factors and lessons from stroke as a national exemplar LHS in Australia include leadership, culture, workforce and resources integrated with (1) established and broad partnerships across the academic-clinical sector divide and stakeholder engagement; (2) the living guidelines program; (3) national data infrastructure, including a national data dictionary that provides the common data framework to support standardized data capture; (4) various implementation strategies including benchmarking and feedback as well as engagement strategies targeting different levels of the health system; and (5) implementation and improvement research to advance stroke systems of care and reduce unwarranted variation in practice (Fig.  1 ). Priority opportunities now include the advancement of interoperability with electronic medical records as an area all clinical quality registry’s programs needs to be addressed, as well as providing more dynamic and interactive data dashboards tailored to the need of clinicians and health service executives.

There is a clear mandate to optimise healthcare improvement with big data offering major opportunities for change. However, we have lacked the approaches to capture evidence from the community and stakeholders, to integrate evidence from research, to capture and leverage data or evidence from practice and to generate and build on evidence from implementation using iterative system-level improvement. The LHS provides this opportunity and is shown to deliver impact. Here, we have outlined the process applied to generate an evidence-based LHS and provide a leading exemplar in stroke care. This highlights the value of moving from single-focus isolated approaches/initiatives to healthcare improvement and the benefit of integration to deliver demonstrable outcomes for our funders and key stakeholders — our community. This work provides insight into strategies that can both apply evidence-based processes to healthcare improvement as well as implementing evidence-based practices into care, moving beyond research as an endpoint, to research as an enabler, underpinning delivery of better healthcare.

Availability of data and materials

Not applicable

Abbreviations

Australian Stroke Clinical Registry

Confidence interval

• Learning Health System

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Acknowledgements

The following authors hold National Health and Medical Research Council Research Fellowships: HT (#2009326), DAC (#1154273), SM (#1196352), MFK Future Leader Research Fellowship (National Heart Foundation #105737). The Funders of this work did not have any direct role in the design of the study, its execution, analyses, interpretation of the data, or decision to submit results for publication.

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Helena Teede and Dominique A. Cadilhac contributed equally.

Authors and Affiliations

Monash Centre for Health Research and Implementation, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede, Emily Callander & Joanne Enticott

Monash Partners Academic Health Science Centre, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede & Alison Johnson

Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Level 2 Monash University Research, Victorian Heart Hospital, 631 Blackburn Rd, Clayton, VIC, Australia

Dominique A. Cadilhac, Tara Purvis & Monique F. Kilkenny

Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia

Dominique A. Cadilhac, Monique F. Kilkenny & Bruce C.V. Campbell

Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Parkville, VIC, Australia

Bruce C.V. Campbell

Department of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Victoria, Australia

School of Health Sciences, Heart and Stroke Program, University of Newcastle, Hunter Medical Research Institute, University Drive, Callaghan, NSW, Australia

Coralie English

School of Medicine and Dentistry, Griffith University, Birtinya, QLD, Australia

Rohan S. Grimley

Clinical Excellence Division, Queensland Health, Brisbane, Australia

John Hunter Hospital, Hunter New England Local Health District and University of Newcastle, Sydney, NSW, Australia

Christopher Levi

School of Nursing, Midwifery and Paramedicine, Australian Catholic University, Sydney, NSW, Australia

Sandy Middleton

Nursing Research Institute, St Vincent’s Health Network Sydney and and Australian Catholic University, Sydney, NSW, Australia

Stroke Foundation, Level 7, 461 Bourke St, Melbourne, VIC, Australia

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HT: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. DAC: conception, design and initial draft, provided essential literature and case study examples, approved the submitted version. TP: revised the manuscript critically for important intellectual content, approved the submitted version. MFK: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. BC: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CE: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. AJ: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. EC: revised the manuscript critically for important intellectual content, approved the submitted version. RSG: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CL: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. SM: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. KH: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. JE: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. All authors read and approved the final manuscript.

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Teede, H., Cadilhac, D.A., Purvis, T. et al. Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke. BMC Med 22 , 198 (2024). https://doi.org/10.1186/s12916-024-03416-w

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Developing Frameworks and Tools for Integration of Digital Health Tools into Clinical Practice

CERSI Collaborators: University of California at San Francisco (UCSF): Andrew Auerbach, MD

FDA Collaborators: Center for Devices and Radiological Health(CDRH): Bakul Patel, MS, MBA (Formerly of CDRH); Vinay Pai, PhD; Leeda Rashid, MD, MPH, ABFM; Catherine Bahr; Arti Tandon, PhD; Charlie Yongpravat, PhD; Anindita Saha, PhD; Jiping Chen, MD, PhD, MPH 

CERSI Subcontractors: Flying Buttress Associates- Jeph Herrin, PhD

CERSI In-Kind Collaborators: OptumLabs - William Crown, PhD; University of San Francisco - Sanket Dhruva, MD

Non-Federal Entity Collaborators: Johnson and Johnson- Karla Childers, MSJ, Paul Coplan, ScD, MBA, Stephen Johnston, MSc

Project Start Date: May 1, 2018 Project End Date: February 28, 2022

Regulatory Science Framework

Charge I “Modernize development and evaluation of FDA-regulated project” and Focus Area “C. Analytical and computational Methods.”

While FDA funding of this project has ended, the research continues to evolve and result in additional findings, as described below. Outcomes/results from this project informed the development of The Digital Variome: Understanding the Implications of Digital Tools on Health .

Regulatory Science Challenge

This research aimed to develop methodologies to improve the quality and safety of FDA-regulated products by using consensus methods to identify gaps in regulation perceived by health systems and healthcare providers, and developing initial blueprints and recommendations for regulators and vendors to follow. Researchers developed methods and tools to improve and streamline clinical and post market evaluation of FDA-regulated products, including scientifically valid approaches to incorporating patient input and approaches to complex and multisource data to inform regulatory decision-making, including use of “real world” data (RWD/RWE).

Project Description and Goals

This project extends the ongoing research work of FDA’s national network of leading academic medical centers, researchers, and innovators to yield information about which real world measures can be used across types of software used in health, and the eventual data sources required to carry out real world performance measurement and post market surveillance of digital health tools.

Research Outcomes/Results

Caring for patients in the electronic era requires multiple people and systems to collaborate, which in turn requires interoperability among connected health records, and integration between myriad digital health tools and devices. Few data or approaches exist to assist with adoption and integration of innovative digital health tools (DHTs) in ways that are safe and effective.

To explore these questions and develop best practices for addressing them, UCSF founded the Accelerated Digital Clinical Ecosystem (ADviCE) . The collaborative, founded in 2018, includes academic and non-academic health systems, single-site and multicenter health systems, medical software developers, payors, and patient groups. Investigators carried out a series of in-person and virtual meetings during which, using a consensus-building framework, we identified problems with Digital Health Technology (DHT) adoption and potential solutions.

Investigators identified the following challenges to DHT adoption: (1) Variable definitions of which DHTs are relevant to clinical care delivery; (2) Lack of consistent, common terms to describe DHTs during selection, (3) Wide variability in how health systems integrate DHTs into practice and, (4) Lack of a framework and tools to evaluate DHTs’ real-world performance through post-market surveillance. Investigators further framed collaborative opportunities that could support solutions to each challenge.

Challenge 1 and 2 - Defining and selecting DHTs : To address the first and second challenges, investigators prototyped a tool known as the ‘Digital Health Common Application’ (DHCA), a framework that gathers a core set of information needed by health systems and patients to make DHT selection choices. In use, the DHCA can increase transparency of DHT’s functions and pitfalls to health system stakeholders as well as becoming a potential ‘package insert’ resource for patients and families. Work on the DHCA continues. Investigators have continued to test the applicability of the Common Application through a series of ADviCE-sponsored internships where ADviCE has hosted between 7 and 18 fellows who have done outreach to digital health companies to gather information using the Common Application format.

Over the course of the last 4 years more than 40 fellows who have contacted more than 180 companies gathering information on 190 total applications. As of Spring 2024, investigators are re-assessing those companies after initial follow-up (between 6 months and 3 years later) to see if they have added functionality, pursued regulation, or accumulated additional evidence. Investigators are also exploring use of large language model chatbots as an approach to gathering information which might later be used to populate the Common Application or regulatory tools.

Challenge 3 - DHT adoption : To address challenges in how health systems adopt DHTs, investigators gathered intake processes and questionnaires from ADviCE participating sites and mapped them into several broad domains – clinical use case, security and privacy review, and governance. These domains are in turn becoming the focus of discussions around best practices for DHT onboarding while also helping frame considerations of RWE generation (as DHT performance may be powerfully impacted by how it is implemented and where).

Challenge 4 – Real world performance : For the fourth challenge identified, investigators took the FDA measurement framework proposed as a general temlaplate for PreCert post market surveillance and undertook consensus work to identify specific domains and measures relevant to each broad domain. For example, within the area of Product Performance à Cybersecurity investigators developed subdomains where metric identification was recognized as a key next step; metric identification was continued in additional findings, as described below. Outcomes/results from this project informed the development of The Digital Variome: Understanding the Implications of Digital Tools on Health .

Research Impacts

In the absence of empiric data on safety or effectiveness, multistakeholder collaboratives have a key role in setting safety and adoption standards for DHTs. ADviCE is a first example of how collaboratives can identify and employ best practices for adoption, improve provider and patient experience, and substantially increase limited data on DHT clinical effectiveness and safety.

Publications

No peer-reviewed publications to date; Investigators plan to analyze and publish follow-up study results.

Dr. Auerbach has published invited editorials in JAMA IM on digital health regulation based in part on his experiences with ADviCE.

Cristina Herrero, president of AIReF: “We’ve still not assimilated the framework of the new tax regulations, under which surpluses are as logical as shortfalls”

“We’ve still not assimilated the framework of the new tax regulations under which surpluses are as logical as shortfalls – such is the counter-cyclical nature of fiscal policy.” Cristina Herrero, president of AIReF , ended the 3rd EsadeEcPol Taxation Forum with an analysis of the future tax regulations. She emphasized “the important change as regards who takes the initiative when making commitments” , which she deemed positive “because it delivers greater credibility than the current framework”, and also the fact that “supervision will focus on the net outlay of revenue measures rather than structural deficit as has been the case until now” .

During her speech, Herrero also pointed out that “in the coming years, fiscal policy will be developed against a new economic and institutional backdrop” , a situation which she believes, “we’ve still not assimilated.” On the subject of fiscal policy, she explained that “despite having the most dynamic growth in the euro zone, its composition means it can’t be sustainable over time” in reference to the lack of investment. As regards her second point, she explained that fiscal regulations are already back, although “there are no real deficit targets” for 2024.

Taking part in the 3rd EsadeEcPol Taxation Forum, entitled “Spanish taxation in the new global economic landscape”, were Jorge Galindo , deputy director of EsadeEcPol; Francisco de la Torre, director of the EsadeEcPol Taxation Forum; Judith Arnal, senior researcher at CEPS and Real Instituto Elcano; Esther Gordo , director of the AIReF Economic Analysis Division; Carlos Thomas , deputy director general of Economy and Research at the Bank of Spain; Omar Rachedi, associate professor in the Esade Department of Economics, Finance and Accounting; David López-Rodríguez , senior economist in the Structural Analysis Department at the Bank of Spain; Clara Martínez-Toledano , professor of Economics at Imperial College Business School; Santiago Lago , chair in the Department of Applied Economics at the University of Vigo; and Miguel Almunia , professor of Economics at CUNEF University.  

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• Thulani P. Makhalanyane   ORCID: orcid.org/0000-0002-8173-1678 10 , 11 &
• Soumaya Kouidhi 12  

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A framework is presented for equitable and effective microbiome research partnerships between African researchers, international partners, healthcare professionals, policymakers and stakeholders.

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Acknowledgements

The authors thank all the members of the H3Africa Microbiome Task Force and the African Microbiome Special Interest Group for the feedback received in the writing process.

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Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa

Ovokeraye H. Oduaran

Department of Child Dental Health, Obafemi Awolowo University, Ile-Ife, Nigeria

Moréniké Oluwátóyìn Foláyan

Oral Health Initiative, Center for Reproductive and Population Health Studies, Nigerian Institute of Medical Research, Yaba, Lagos, Nigeria

Kamuzu University of Health Sciences, School of Life Sciences and Allied Health Professions, Blantyre, Malawi

Arox W. Kamng’ona

Department of Obstetrics and Gynaecology, School of Medicine, Makerere University College of Health Sciences, Kampala, Uganda

Annettee Nakimuli

Department of Biology, Chemistry and Physics, Faculty of Health, Natural Resources and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia

Lamech M. Mwapagha

African Microbiome Institute, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Stellenbosch, South Africa

Mathabatha E. Setati

Department of Medical Diagnostics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Michael Owusu

Computational Biology Division Department of Integrative Biomedical Sciences, IDM, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

Nicola Mulder

Department of Microbiology, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa

Thulani P. Makhalanyane

School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa

Laboratory of Biotechnology and Valorisation of Bio-Geo Resources (LR11ES31), Higher Institute of Biotechnology of Sidi Thabet, BiotechPole of Sidi Thabet, University of Manouba, Ariana, Tunisia

Soumaya Kouidhi

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Correspondence to Ovokeraye H. Oduaran .

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Oduaran, O.H., Foláyan, M.O., Kamng’ona, A.W. et al. Microbiome research in Africa must be based on equitable partnerships. Nat Med (2024). https://doi.org/10.1038/s41591-024-03026-2

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DOI : https://doi.org/10.1038/s41591-024-03026-2

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The market size is anticipated to be $0.06 billion in 2026 and is expected to reach $7.72 billion by 2040, growing at a CAGR of 41.55% during the forecast period 2026-2040.

The constrained peptide drugs market is expected to experience significant growth, driven by innovative constrained peptide pipelines that now target more than just receptors. Advances in chemical technologies, the success of synthetic peptides in therapeutics, and their affordability across various diseases are also contributing to the projected growth in the forecast period.

Market Introduction

The Europe constrained peptide drugs market is poised for substantial growth in the coming years. One of the key drivers of this expansion is the region's robust pharmaceutical industry, characterized by a focus on research and development, innovation, and a strong regulatory framework. European pharmaceutical companies have been at the forefront of developing constrained peptide drugs, leveraging cutting-edge technologies and expertise in peptide chemistry.

Additionally, Europe has witnessed a growing demand for novel therapeutic approaches, particularly in the treatment of various diseases such as cancer, autoimmune disorders, and metabolic conditions. Constrained peptides offer a promising solution due to their specificity and potential for enhanced efficacy and safety profiles.

Furthermore, collaborative efforts between academia, research institutions, and pharmaceutical companies in Europe have accelerated the discovery and development of constrained peptide-based therapeutics. This collaborative ecosystem fosters innovation and positions Europe as a significant player in the constrained peptide drugs market.

How can this report add value to an organization?

Workflow/Innovation Strategy: Over the past decade, peptide drug discovery and development has witnessed a renaissance and scientific thrust as the industry has come to acknowledge the capability of peptide therapeutics in addressing unmet medical needs and the potential of this class of molecules to become a significant accompaniment or even favored alternative treatment to biologics and small molecules.

Peptide therapeutics have demonstrated a novel and selective yet safe mode of action for a wide range of indications. The existing and future development of constrained peptide drugs will continue to burgeon upon the strengths of constrained peptides and innovative technologies employed in the discovery and development, including peptide drug conjugates, multifunctional peptides, and cell-penetrating peptides. Furthermore, limitations associated with presently available peptides have resulted in an urgent need for new design, administration, and synthesis in peptide therapeutics, thereby leading to advancements in the development of constrained peptides.

Growth/Marketing Strategy: Constrained peptides provide noteworthy advantages over linear peptides. An increase in interest in constrained peptides due to their properties led to advancements in peptide synthesis technologies.

Competitive Strategy: Key players in the Europe constrained peptide drugs market have been analyzed and profiled in the study, including manufacturers involved in new product development, acquisitions, expansions, and strategic collaborations. Moreover, a detailed competitive benchmarking of the players operating in the Europe constrained peptide drugs market has been done to help the reader understand how players stack against each other, presenting a clear market landscape.

Additionally, comprehensive competitive strategies such as partnerships, agreements, and collaborations will aid the reader in understanding the untapped revenue pockets in the market.

Company Profiles

• Bicycle Therapeutics plc
• Santhera Pharmaceuticals
• Union Chimique Belge S.A. (UCB)
• Biosynth (Pepscan)
• Zealand Pharma

Key Attributes:

Key Topics Covered:

Executive Summary

1 Definition

1.1 Inclusion and Exclusion Criteria

2 Research Scope

2.1 Key Questions Answered in the Report

3 Research Methodology

3.1 Constrained Peptides: Research Methodology

3.2 Primary Data Sources

3.3 Secondary Data Sources

3.4 Market Estimation Model

3.5 Criteria for Company Profiling

4 Markets Overview

4.1 Introduction

4.1.1 Structure and Design of Constrained Peptides

4.1.2 Types of Constrained Peptides

4.2 Evolution of Constrained Peptides

4.3 Development of Constrained Peptides as Drugs

4.4 Potential Therapy Areas

4.5 Value Chain-Key Stakeholders

4.6 Key Industry Trends (by Region)

4.7 Key Industry Trends by Route of Administration

4.8 Key Industry Trends-Technological Advancements

4.9 Current Market Size and Growth Potential, $Billion, 2024-2040

4.1 COVID-19 Impact on Constrained Peptides Drugs Market

4.10.1 Impact on Constrained Peptide Drugs Companies

4.10.2 Clinical Trial Disruptions and Resumptions

5.1 Constrained Peptide Drugs Market (by Country), $Billion, 2024-2031

5.1.1 Europe

5.1.1.1 U.K.

5.1.1.2 Germany

5.1.1.3 France

5.1.1.4 Italy

5.1.1.5 Spain

For more information about this report visit https://www.researchandmarkets.com/r/szrau8

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