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http://orcid.org/0000-0003-0180-0213 Anam Shahil Feroz 1 , 2 ,
Naureen Akber Ali 3 ,
Noshaba Akber Ali 1 ,
Ridah Feroz 4 ,
Salima Nazim Meghani 1 ,
Sarah Saleem 1
1 Community Health Sciences , Aga Khan University , Karachi , Pakistan
2 Institute of Health Policy, Management and Evaluation , University of Toronto , Toronto , Ontario , Canada
3 School of Nursing and Midwifery , Aga Khan University , Karachi , Pakistan
4 Aga Khan University Institute for Educational Development , Karachi , Pakistan
Correspondence to Ms Anam Shahil Feroz; anam.sahyl{at}gmail.com

Introduction The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. In addition, with the widespread use of social media, rumours, myths and inaccurate information about the virus are spreading rapidly, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes of community members towards COVID-19 and its impact on their daily lives and mental well-being.

Methods and analysis This formative research will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews with community members. The study will be conducted in the Karimabad Federal B Area and in the Garden (East and West) community settings in Karachi, Pakistan. The community members of these areas have been selected purposively for the interview. Study data will be analysed thematically using NVivo V.12 Plus software.

Ethics and dissemination Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The results of the study will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

mental health
public health

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https://doi.org/10.1136/bmjopen-2020-041641

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Strengths and limitations of this study

The mental health impact of the COVID-19 pandemic is likely to last much longer than the physical health impact, and this study is positioned well to explore the perceptions and attitudes of community members towards the pandemic and its impact on their daily lives and mental well-being.

This study will guide the development of context-specific innovative mental health programmes to support communities in the future.

One limitation is that to minimise the risk of infection all study respondents will be interviewed online over Zoom and hence the authors will not have the opportunity to build rapport with the respondents or obtain non-verbal cues during interviews.

The COVID-19 pandemic has affected almost 180 countries since it was first detected in Wuhan, China in December 2019. 1 2 The COVID-19 outbreak has been declared a public health emergency of international concern by the WHO. 3 The WHO estimates the global mortality to be about 3.4% 4 ; however, death rates vary between countries and across age groups. 5 In Pakistan, a total of 10 880 cases and 228 deaths due to COVID-19 infection have been reported to date. 6

The worldwide COVID-19 pandemic has not only incurred massive challenges to the global supply chains and healthcare systems but also has a detrimental effect on the overall health of individuals. 7 The pandemic has led to lockdowns and has created destructive impact on the societies at large. Most company employees, including daily wage workers, have been prohibited from going to their workplaces or have been asked to work from home, which has caused job-related insecurities and financial crises in the communities. 8 Educational institutions and training centres have also been closed, which resulted in children losing their routine of going to schools, studying and socialising with their peers. Delay in examinations is likewise a huge stressor for students. 8 Alongside this, parents have been struggling with creating a structured milieu for their children. 9 COVID-19 has hindered the normal routine life of every individual, be it children, teenagers, adults or the elderly. The crisis is engendering burden throughout populations and communities, particularly in developing countries such as Pakistan which face major challenges due to fragile healthcare systems and poor economic structures. 10

The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. 8 Further, the highly contagious nature of COVID-19 has also escalated confusion, fear and panic among community residents. Moreover, social distancing is often an unpleasant experience for community members and for patients as it adds to mental suffering, particularly in the local setting where get-togethers with friends and families are a major source of entertainment. 9 Recent studies also showed that individuals who are following social distancing rules experience loneliness, causing a substantial level of distress in the form of anxiety, stress, anger, misperception and post-traumatic stress symptoms. 8 11 Separation from family members, loss of autonomy, insecurity over disease status, inadequate supplies, inadequate information, financial loss, frustration, stigma and boredom are all major stressors that can create drastic impact on an individual’s life. 11 Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. 12 With the widespread use of social media, 13 rumours, myths and inaccurate information about COVID-19 are also spreading rapidly, not only among adults but are also carried on to children, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. 9 The psychological symptoms associated with COVID-19 at the community level are also manifested as anxiety-driven panic buying, resulting in exhaustion of resources from the market. 14 Some level of panic also dwells in the community due to the unavailability of essential protective equipment, particularly masks and sanitisers. 15 Similarly, mental health issues, including depression, anxiety, panic attacks, psychotic symptoms and even suicide, were reported during the early severe acute respiratory syndrome outbreak. 16 17 COVID-19 is likely posing a similar risk throughout the world. 12

The fear of transmitting the disease or a family member falling ill is a probable mental function of human nature, but at some point the psychological fear of the disease generates more anxiety than the disease itself. Therefore, mental health problems are likely to increase among community residents during an epidemic situation. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes towards COVID-19 among community residents and the impact of these perceptions and attitude on their daily lives and mental well-being.

Methods and analysis

Study design.

This study will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews (IDIs) with community members. The IDIs aim to explore perceptions of community members towards COVID-19 and its impact on their mental well-being.

Study setting and study participants

The study will be conducted in two communities in Karachi City: Karimabad Federal B Area Block 3 Gulberg Town, and Garden East and Garden West. Karimabad is a neighbourhood in the Karachi Central District of Karachi, Pakistan, situated in the south of Gulberg Town bordering Liaquatabad, Gharibabad and Federal B Area. The population of this neighbourhood is predominantly Ismailis. People living here belong mostly to the middle class to the lower middle class. It is also known for its wholesale market of sports goods and stationery. Garden is an upmarket neighbourhood in the Karachi South District of Karachi, Pakistan, subdivided into two neighbourhoods: Garden East and Garden West. It is the residential area around the Karachi Zoological Gardens; hence, it is popularly known as the ‘Garden’ area. The population of Garden used to be primarily Ismailis and Goan Catholics but has seen an increasing number of Memons, Pashtuns and Baloch. These areas have been selected purposively because the few members of these communities are already known to one of the coinvestigators. The coinvestigator will serve as a gatekeeper for providing entrance to the community for the purpose of this study. Adult community members of different ages and both genders will be interviewed from both sites, as mentioned in table 1 . Interview participants will be selected following the eligibility criteria.

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Study participants for indepth interviews

IDIs with community members

We will conduct IDIs with community members to explore the perceptions and attitudes of community members towards COVID-19 and its effects on their daily lives and mental well-being. IDI participants will be identified via the community WhatsApp group, and will be invited for an interview via a WhatsApp message or email. Consent will be taken over email or WhatsApp before the interview begins, where they will agree that the interview can be audio-recorded and that written notes can be taken. The interviews will be conducted either in Urdu or in English language, and each interview will last around 40–50 min. Study participants will be assured that their information will remain confidential and that no identifying features will be mentioned on the transcript. The major themes will include a general discussion about participants’ knowledge and perceptions about the COVID-19 pandemic, perceptions on safety measures, and perceived challenges in the current situation and its impact on their mental well-being. We anticipate that 24–30 interviews will be conducted, but we will cease interviews once data saturation has been achieved. Data saturation is the point when no new themes emerge from the additional interviews. Data collection will occur concurrently with data analysis to determine data saturation point. The audio recordings will be transcribed by a transcriptionist within 24 hours of the interviews.

An interview guide for IDIs is shown in online supplemental annex 1 .

Supplemental material

Eligibility criteria.

The following are the criteria for inclusion and exclusion of study participants:

Inclusion criteria

Residents of Garden (East and West) and Karimabad Federal B Area of Karachi who have not contracted the disease.

Exclusion criteria

Those who refuse to participate in the study.

Those who have experienced COVID-19 and are undergoing treatment.

Those who are suspected for COVID-19 and have been isolated/quarantined.

Family members of COVID-19-positive cases.

Data collection procedure

A semistructured interview guide has been developed for community members. The initial questions on the guide will help to explore participants’ perceptions and attitudes towards COVID-19. Additional questions on the guide will assess the impact of these perceptions and attitude on the daily lives and mental health and well-being of community residents. All semistructured interviews will be conducted online via Zoom or WhatsApp. Interviews will be scheduled at the participant’s convenient day and time. Interviews are anticipated to begin on 1 December 2020.

Patient and public involvement

No patients were involved.

Data analysis

We will transcribe and translate collected data into English language by listening to the audio recordings in order to conduct a thematic analysis. NVivo V.12 Plus software will be used to import, organise and explore data for analysis. Two independent researchers will read the transcripts at various times to develop familiarity and clarification with the data. We will employ an iterative process which will help us to label data and generate new categories to identify emergent themes. The recorded text will be divided into shortened units and labelled as a ‘code’ without losing the main essence of the research study. Subsequently, codes will be analysed and merged into comparable categories. Lastly, the same categories will be grouped into subthemes and final themes. To ensure inter-rater reliability, two independent investigators will perform the coding, category creation and thematic analyses. Discrepancies between the two investigators will be resolved through consensus meetings to reduce researcher bias.

Ethics and dissemination

Study participants will be asked to provide informed, written consent prior to participation in the study. The informed consent form can be submitted by the participant via WhatsApp or email. Participants who are unable to write their names will be asked to provide a thumbprint to symbolise their consent to participate. Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The study results will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

The findings of this study will help us to explore the perceptions and attitudes towards the COVID-19 pandemic and its impact on the daily lives and mental well-being of individuals in the community. Besides, an indepth understanding of the needs of the community will be identified, which will help us develop context-specific innovative mental health programmes to support communities in the future. The study will provide insights into how communities are managing their lives under such a difficult situation.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1

ASF and NAA are joint first authors.

Contributors ASF and NAA conceived the study. ASF, NAA, RF, NA, SNM and SS contributed to the development of the study design and final protocols for sample selection and interviews. ASF and NAA contributed to writing the manuscript. All authors reviewed and approved the final version of the paper.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Open access
Published: 20 June 2024

The mediating role of health literacy in the relationship between self-care and planned behavior against Covid-19

Sirous Panahi 1 &
Hossein Ghalavand 2

BMC Infectious Diseases volume 24 , Article number: 608 ( 2024 ) Cite this article

Metrics details

Planned behaviors and self-care against the coronavirus are two important factor in controlling its spread and self-care behaviors depend on the level of health literacy. This research was conducted to determine the mediating role of health literacy in the relationship between elements of planned behavior and self-care in dealing with the Covid-19.

In this descriptive-analytical quantitative study, the sample size was calculated using Cochrane’s formula and considering a p-value of 0.51, α = 0.05, and d = 0.05, and 313 students were selected based on stratified and random method. To gather data and assess various aspects of variables, a questionnaires were utilized, focusing on health literacy, self-car and planned behavior. The relationship between the variables was examined by SPSS version 26 and via descriptive statistics, including the mean and standard deviation, and inferential statistics such as Pearson’s correlation coefficient ( P = 0.05), path analysis, and determining the standard coefficients between self-care and planned behavior, mediated by the indicators of the health literacy.

A significant difference was found between the level of health literacy of women and men. The comparison of the mean health literacy and self-care behavior in terms of other variables did not show any significant difference. Meanwhile, the comparison of health status control behaviors, hand washing, and mask use did not show any significant difference between the two groups. A positive and significant correlation was found between self-care behaviors, attitude, subjective norms, perceived behavioral control, and behavioral intention. The relationship of health literacy and psychological variables of attitude, subjective norms, and perceived behavioral control with self-care against COVID-19 was significant.

The direct and significant impact of health literacy on individuals’ self-care behaviors against the coronavirus was not observed. However, health literacy did have a significant effect on subjective norms. This finding is important because subjective norms significantly influenced individuals’ behavioral intention, which in turn had a significant effect on self-care behaviors against the coronavirus. Thus, health literacy played a mediating role in this relationship. Furthermore, attitude emerged as the strongest predictor of behavioral intention, exerting a direct effect. Conversely, perceived behavioral control did not directly and significantly affect students’ self-care behaviors.

Peer Review reports

Self-care is a key control approach and a cognitive activity whereby people play a major role in maintaining their health. People’s ability to take care of themselves and adhere to the recommended protocols is the main method of preventing infection with the coronavirus [ 1 , 2 ]. Self-care against the coronavirus includes actions such as observing social distancing and wearing a mask. Awareness and adherence have played a significant role in controlling the COVID-19 pandemic. Self-care involves acquired, conscious, and purposeful actions that people undertake for the health of themselves, their children, and their families [ 3 , 4 ].

There is a direct link between self-care, adherence to medical and health recommendations, and health literacy [ 5 ]. Health literacy refers to people’s ability to receive, process, and comprehend health information, which can lead to better decision-making at different times [ 6 , 7 ]. It is a factor influencing people’s self-care for disease control and prevention. People’s low health literacy level and their inability to understand the information provided by health professionals can negatively impact their health and increase their medical expenses [ 4 ]. Accordingly, measuring health literacy can contribute to detecting people’s abilities and designing necessary educational interventions to improve their health literacy [ 8 ]. Therefore, paying attention to the link between health literacy and self-care against the coronavirus can prove a proper strategy to support preventive activities during the outbreak of such infectious diseases.

Given people’s health-related and behavioral problems in dealing with health challenges, theories and behavioral patterns can be employed to determine and identify the factors affecting health-related behaviors [ 9 ]. In fact, the use of theories to describe people’s behavior during health crises can enhance the efficiency, effectiveness, and chance of success in obtaining the desired outcomes [ 10 ]. Various guidelines and recommendations were emphasized during the COVID-19 outbreak, and people were expected to play a key role in self-care and control the spread of the virus by adhering to these guidelines; in practice, however, some communities did not succeed in this regard. There are various theories about health-related behaviors whose core deals with doing or not doing predetermined behaviors [ 11 , 12 , 13 ]. In the sphere of health, the theory of planned behavior (TPB) is a theory of behavior change whose efficiency and effectiveness have been proven in previous studies [ 14 , 15 ]. TPB asserts that individual beliefs regarding a specific behavior influence their attitude towards it, the prevailing subjective norms, and the perceived behavioral control, ultimately leading to the intention to engage in that behavior. TPB incorporated the concept of behavioral control as a crucial determinant of health behavior, alongside attitude and subjective norms. TPB establishes a causal chain linking beliefs (behavioral, normative, and control beliefs) to intentions and behaviors through attitudes, norms, and perceived control, providing a structured approach to identify key factors influencing an individual’s decision-making process. Given the changeability of beliefs and attitudes, they serve as prime targets for interventions aimed at modifying behavior [ 14 , 15 , 16 , 17 ].

In TPB, the main construct that determines behavior is the person’s intention, and the three constructs of attitude, subjective norms, and perceived behavioral control affect this intention [ 18 ]. Based on the TPB, the more favorable one’s attitude towards a behavior, the more likely the intention to perform the behavior. In this theory, subjective norms include a person’s subjective perception of others’ approval or disapproval of performing a behavior, and membership in support groups and increasing social support can lead to performing or not performing a certain behavior. Perceived behavioral control is the degree of control one feels to perform or not perform a behavior, which has a significant association with personal will [ 19 , 20 ]. The TPB has been employed in the domain of health, especially in patients’ self-care, and its efficacy has been confirmed in predicting and comprehending healthy and unhealthy behaviors and their related outcomes [ 1 , 2 , 5 , 15 , 19 ].

Overall, paying attention to planned behaviors and self-care against the coronavirus is a major factor in controlling its spread, and self-care behaviors depend on the level of health literacy. Although some studies have been conducted on health literacy and self-care behavior for different diseases [ 4 , 11 , 13 , 21 ]. This study was planned to determine the relationship between health literacy and self-care behavior, mediated by the constructs of planned behavior, was examined among students of Abadan University of Medical Sciences (Iran).

Population and samples

This descriptive-analytical quantitative study was conducted to determine the relationship between health information literacy, elements of planned behavior, and self-care in dealing with the coronavirus. The research population included all students studying at Abadan University of Medical Sciences (AbadanUMS) in the academic year 2022–2023 ( n = 1698). The sample size was calculated using Cochrane’s formula and considering a p-value of 0.51, α = 0.05, and d = 0.05, and 313 students were selected, divided by their fields of study. Sampling was stratified and random.

Measurement

A four-part questionnaire was used to collect data. The first part include demographic characteristics such as sex, age, and a history of contracting the coronavirus. In the second part, to measure health literacy, the short form of the standard Health Literacy Questionnaire was used as the most common and comprehensive standard instrument for measuring health literacy [ 22 ]. This questionnaire has 33 items based on a Likert scale (from 1 = never to 5 = always); its validity is 0.83 (Cronbach’s coefficient), and its reliability has been confirmed (coefficient of 0.93) in previous studies [ 19 , 20 ]. In the third part, to measure the constituents of the TPB, 17 questions in four groups were considered to measure the scales of attitudes (Q1-Q3), subjective norms (Q4-Q6), perceived behavioral control (Q7-Q9), and behavioral intention (Q10-Q17), based on a five-point Likert scale (1 = completely agree to 5 = completely disagree). The content validity index (CVI = 0.83) and content validity ratio (CVR = 0.86) confirmed the face and content validity of this part of the questionnaire [ 23 , 24 ]. In the fourth part, a literature review was conducted, the accepted international protocols for self-care and preventing the spread of the coronavirus were extracted, and experts were consulted. This questionnaire include social distancing (Q1, Q7), vaccine injection (Q8), check health status (Q5, Q6), washing hands (Q3, Q4) and Use a disposable mask (Q2). The electronic form of this questionnaire was designed, and a link to it was sent to the participants.

Statistical analysis

SPSS (v. 26) was used for data analysis. The association between the variables was examined via descriptive statistics, including the mean and standard deviation (SD), and inferential statistics such as Pearson’s correlation coefficient ( P = 0.05), path analysis, and determining the standard coefficients between self-care behaviors and health literacy, mediated by the indicators of the TPB.

A total of nine student from the selected samples did not participate in this study. Table 1 shows the level of health literacy and self-care behavior based on different demographic characteristics of the 305 participants. Most of the participants were women, aged 18–20 years, and were seniors. A significant difference was found between the level of health literacy of women and men, where women had a higher mean health literacy. Besides, there was a significant difference in the mean health literacy of the students based on the academic semester, and the level of health literacy increased with the semesters. The comparison of the mean health literacy and self-care behavior in terms of other variables did not show any significant difference.

In this research, the mean comparison test was used for two independent groups of men and women. Based on Table 2 , the mean of attitude, subjective norms, and behavioral intentions differed between men and women based on the levels of health literacy. The subscales related to students’ self-care showed a significant difference between men and women based on their compliance with social distancing and vaccination. Meanwhile, the comparison of health status control behaviors, hand washing, and mask use did not show any significant difference between the two groups.

Based on Table 3 , a positive and significant correlation was found between self-care behaviors, attitude, subjective norms, perceived behavioral control, and behavioral intention.

Figure 1 displays the results of path analysis and standard coefficients. Health literacy did not have a direct and significant effect on self-care behaviors against the coronavirus. Still, its effect on subjective norms was significant, and due to the significant effect of subjective norms on behavioral intention and the significant effect of behavioral intention on self-care against the coronavirus, health literacy was a mediator variable. Moreover, attitude was the greatest predictor of behavioral intention directly; perceived behavioral control did not directly and significantly affect the students’ self-care, but its effect was mediated by behavioral intention. The fit indices of the model (Fig. 1 ) indicate the fit of the data to the model. In general, the model predicted 0.346 of the variance of the final variable, i.e., self-care against COVID-19.

Path analysis and standardized coefficients between constructs of TBP, self-care behaviors and health literacy

The findings revealed that there is a significant difference between the mean health literacy of male and female students. Women are more literate in understanding medical forms, medication usage instructions, and written information, and the level of health literacy between women and men may be different in various social strata and cultures [ 25 , 26 , 27 ]. Men make less effort to obtain information due to subjective beliefs, lower perceived sensitivity to illness, and less understanding of health threats. This difference can possibly make women more willing to report diseases compared to men [ 5 , 27 , 28 ]. Not having enough time to search for health information, especially when the disease is not quite threatening or serious, could be another reason for the low level of health literacy in men [ 29 ].

The findings of the present study demonstrated a significant difference in health literacy between the participants based on academic semesters. In general, the power of recognition and understanding to comprehend health literacy increases with the level of education. People’s problems with using different media, along with their little familiarity with medical terms, can have a negative impact on their ability to interact successfully with healthcare systems [ 25 , 30 ]. The ability to access simplified health information is another factor in improving health literacy. The use of simple images and proper examples can facilitate people’s understanding of health-related topics [ 31 , 32 ]. It is necessary for healthcare systems to modify their information services according to people’s health literacy level and provide training through simple strategies such as face-to-face counseling, group discussions, and educational pamphlets [ 23 , 33 ].

The results of this research showed a significant difference in the mean attitude score between the two groups with a low and adequate health literacy level. Positive attitudes towards self-care and adherence to correct health-related behaviors are crucial, and there is a direct relationship between health literacy and attitudes [ 34 ]. Positive attitudes and a high level of health literacy encourage patients to make appropriate decisions. When people feel that a behavior leads to a positive outcome, they adopt and maintain that behavior [ 35 ].

The difference in subjective norms scores between students with the health literacy level was another finding of this study. In diseases such as diabetes, the patient’s family can play a central role in the administration of self-care training methods. Patients whose families have adequate information about the disease and recommend correct health-related behaviors have more effective control and better compliance with treatment [ 35 , 36 ]. As a result, the formation of support groups and the participation of important people, such as the family, in self-care programs can help promote the health level of patients by strengthening the mentality of support and confirming the continuation of correct health-related behavior [ 37 , 38 , 39 ].

The findings of this research revealed a significant relationship between perceived behavioral control, health literacy level, and self-care against the coronavirus. Perceived behavioral control refers to a person’s judgment about being under control and their intentional ability to perform a specific action, which is an important factor in their performance. Perceived behavioral control is a key predictive factor in people’s intention to perform health-related behaviors and can be increased by creating a suitable environment to acquire the skills and knowledge required for behavioral control and personal empowerment [ 21 , 36 ]. People with a low level of behavioral control make less effort to perform the right health-related behaviors or change wrong behaviors [ 7 , 31 , 38 ]. Modeling, repeating in practice, simplifying, and dividing a behavior into smaller steps, as well as strategies such as goal-setting, planning action, and planning to overcome obstacles, will ultimately have a positive effect on self-care [ 40 , 41 , 42 ].

Results of the present study, like previous studies, demonstrated a significant difference between the two groups of participants with poor and adequate health literacy in terms of self-care behaviors, including social distancing and vaccination [ 26 , 43 , 44 ]. Those with low health literacy are less likely to understand written and spoken information provided by healthcare professionals and follow their instructions. These people have a worse health status, a higher rate of hospitalization, more visits to the doctor, and weaker self-care skills [ 30 , 40 , 41 ]. In general, people with a low level of health literacy often use passive communication methods, do not participate in decision-making, and face numerous problems in interacting with their physicians [ 5 , 13 , 45 ]. Therefore, healthcare professionals should empower people and patients through various trainings to improve their self-confidence, increase their participation, and help them establish effective communication with healthcare providers. In fact, self-care is based on knowledge and is influenced by people’s health-related knowledge. The higher people’s health-related knowledge, the better their ability to identify self-care needs, plan how to meet these needs, and make judgments and decisions about prioritizing their needs [ 35 , 46 ].

In the present study, the TBP theory constructs predicted 0.346 of self-care behaviors. Regression analysis in previous studies showed that 41.5% of the variance of intention and 26.2% of the variance of behavior was predicted by the constructs of TBP theory [ 47 ]. Furthermore, similar to the findings of other studies, attitude was the most important predictor of self-care behavior in students during the COVID-19 outbreak [ 8 , 26 , 35 , 46 ]. The severity and sensitivity of complications, costs, and benefits of following self-care can be a major part of the behavior variance [ 48 , 49 ].

This study investigated the mediating role of health literacy in the relationship between the TPB, and self-care behaviors against the coronavirus among the students of Abadan University of Medical Sciences. The results revealed that the health literacy of female students was higher than that of male students. The relationship of health literacy and psychological variables of attitude, subjective norms, and perceived behavioral control with self-care against COVID-19 was significant.

The present study was not possible to obtain and analyze causal relationships due to budget and time constraints. The students filled out the instruments as self-reports, and there is a possibility of bias in completing the questionnaires. The researcher’s lack of complete control over the participants and their follow-up of, especially regarding the observance of health recommendations related to the coronavirus, was the other limitation of this research. The students of a single university participated in this study, and the generalization of the results is very difficult and limited. As such, it is recommended that similar studies be conducted in other regions and for other diseases. Although the effectiveness of the TBP theory was proven in predicting and determining the factors affecting self-care behaviors during the COVID-19 outbreak, it should be noted that behavior is a multidimensional and multifactorial issue. Therefore, it is suggested that other psychological variables in the form of behavior change models and theories be used in future studies to explore and predict the relationship between self-care and health literacy.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Theory of Planned Behavior

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Acknowledgements

Many thanks to the students who participate in this research. The authors are grateful for the support of the AbadanUMS vice-chancellor for conducting this research.

This study was supported by Abadan University of medical sciences, Research code: 1526.

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The outbreak of COVID-19, a disease caused by severe acute respiratory syndrome coronavirus 2, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the globalspread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, proteomics could help to understand the mechanisms associated with SARS-CoV-2 infection and to identify new therapeutic targets for antiviral drug repurposing and/or discovery. A TMT-based quantitative proteomics and phosphoproteomics analysis was performed to study the proteome remodeling of human lung alveolar cells transduced to express human ACE2 (A549-ACE2) after infection with SARS-CoV-2. Targeted PRM analysis was performed to assess the detectability in serum and prognostic value of selected proteins. A total of 6802 proteins and 6428 phospho-sites were identified in A549-ACE2 cells after infection with SARS-CoV-2. Regarding the viral proteome, 8 proteins were differentially expressed after 6 h of infection and reached a steady state after 9 h. In addition, we detected several phosphorylation sites of SARS-CoV-2 proteins, including two novel phosphorylation events at S410 and S416 of the viral nucleoprotein.

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A quantitative and qualitative analysis of the COVID-19 pandemic model

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1 Department of Mathematics, University of Raparin, Ranya, Sulaimani, Iraq.
2 Department of Mathematics and Statistics, Hazara University, Mansehra 21300, Pakistan.
PMID: 32523257
PMCID: PMC7247488
DOI: 10.1016/j.chaos.2020.109932

Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.

Keywords: Computational simulations; Coronavirus disease (COVID-19); Mathematical modeling; Model reduction; Sensitivity analysis.

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The model interaction individuals for the COVID–19 epidemic outbreak with reaction rates.

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05 May 2021

COVID research: a year of scientific milestones

For just over a year of the COVID-19 pandemic, Nature highlighted key papers and preprints to help readers keep up with the flood of coronavirus research. Those highlights are below. For continued coverage of important COVID-19 developments, go to Nature’s news section .

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Greater Good Science Center • Magazine • In Action • In Education

11 Questions to Ask About COVID-19 Research

Debates have raged on social media, around dinner tables, on TV, and in Congress about the science of COVID-19. Is it really worse than the flu? How necessary are lockdowns? Do masks work to prevent infection? What kinds of masks work best? Is the new vaccine safe?

You might see friends, relatives, and coworkers offer competing answers, often brandishing studies or citing individual doctors and scientists to support their positions. With so much disagreement—and with such high stakes—how can we use science to make the best decisions?

Here at Greater Good , we cover research into social and emotional well-being, and we try to help people apply findings to their personal and professional lives. We are well aware that our business is a tricky one.

Summarizing scientific studies and distilling the key insights that people can apply to their lives isn’t just difficult for the obvious reasons, like understanding and then explaining formal science terms or rigorous empirical and analytic methods to non-specialists. It’s also the case that context gets lost when we translate findings into stories, tips, and tools, especially when we push it all through the nuance-squashing machine of the Internet. Many people rarely read past the headlines, which intrinsically aim to be relatable and provoke interest in as many people as possible. Because our articles can never be as comprehensive as the original studies, they almost always omit some crucial caveats, such as limitations acknowledged by the researchers. To get those, you need access to the studies themselves.

And it’s very common for findings and scientists to seem to contradict each other. For example, there were many contradictory findings and recommendations about the use of masks, especially at the beginning of the pandemic—though as we’ll discuss, it’s important to understand that a scientific consensus did emerge.

Given the complexities and ambiguities of the scientific endeavor, is it possible for a non-scientist to strike a balance between wholesale dismissal and uncritical belief? Are there red flags to look for when you read about a study on a site like Greater Good or hear about one on a Fox News program? If you do read an original source study, how should you, as a non-scientist, gauge its credibility?

Here are 11 questions you might ask when you read about the latest scientific findings about the pandemic, based on our own work here at Greater Good.

1. Did the study appear in a peer-reviewed journal?

In peer review, submitted articles are sent to other experts for detailed critical input that often must be addressed in a revision prior to being accepted and published. This remains one of the best ways we have for ascertaining the rigor of the study and rationale for its conclusions. Many scientists describe peer review as a truly humbling crucible. If a study didn’t go through this process, for whatever reason, it should be taken with a much bigger grain of salt.

“When thinking about the coronavirus studies, it is important to note that things were happening so fast that in the beginning people were releasing non-peer reviewed, observational studies,” says Dr. Leif Hass, a family medicine doctor and hospitalist at Sutter Health’s Alta Bates Summit Medical Center in Oakland, California. “This is what we typically do as hypothesis-generating but given the crisis, we started acting on them.”

In a confusing, time-pressed, fluid situation like the one COVID-19 presented, people without medical training have often been forced to simply defer to expertise in making individual and collective decisions, turning to culturally vetted institutions like the Centers for Disease Control (CDC). Is that wise? Read on.

2. Who conducted the study, and where did it appear?

“I try to listen to the opinion of people who are deep in the field being addressed and assess their response to the study at hand,” says Hass. “With the MRNA coronavirus vaccines, I heard Paul Offit from UPenn at a UCSF Grand Rounds talk about it. He literally wrote the book on vaccines. He reviewed what we know and gave the vaccine a big thumbs up. I was sold.”

From a scientific perspective, individual expertise and accomplishment matters—but so does institutional affiliation.

Why? Because institutions provide a framework for individual accountability as well as safety guidelines. At UC Berkeley, for example , research involving human subjects during COVID-19 must submit a Human Subjects Proposal Supplement Form , and follow a standard protocol and rigorous guidelines . Is this process perfect? No. It’s run by humans and humans are imperfect. However, the conclusions are far more reliable than opinions offered by someone’s favorite YouTuber .

Recommendations coming from institutions like the CDC should not be accepted uncritically. At the same time, however, all of us—including individuals sporting a “Ph.D.” or “M.D.” after their names—must be humble in the face of them. The CDC represents a formidable concentration of scientific talent and knowledge that dwarfs the perspective of any one individual. In a crisis like COVID-19, we need to defer to that expertise, at least conditionally.

“If we look at social media, things could look frightening,” says Hass. When hundreds of millions of people are vaccinated, millions of them will be afflicted anyway, in the course of life, by conditions like strokes, anaphylaxis, and Bell’s palsy. “We have to have faith that people collecting the data will let us know if we are seeing those things above the baseline rate.”

3. Who was studied, and where?

Animal experiments tell scientists a lot, but their applicability to our daily human lives will be limited. Similarly, if researchers only studied men, the conclusions might not be relevant to women, and vice versa.

Many psychology studies rely on WEIRD (Western, educated, industrialized, rich and democratic) participants, mainly college students, which creates an in-built bias in the discipline’s conclusions. Historically, biomedical studies also bias toward gathering measures from white male study participants, which again, limits generalizability of findings. Does that mean you should dismiss Western science? Of course not. It’s just the equivalent of a “Caution,” “Yield,” or “Roadwork Ahead” sign on the road to understanding.

This applies to the coronavirus vaccines now being distributed and administered around the world. The vaccines will have side effects; all medicines do. Those side effects will be worse for some people than others, depending on their genetic inheritance, medical status, age, upbringing, current living conditions, and other factors.

For Hass, it amounts to this question: Will those side effects be worse, on balance, than COVID-19, for most people?

“When I hear that four in 100,000 [of people in the vaccine trials] had Bell’s palsy, I know that it would have been a heck of a lot worse if 100,000 people had COVID. Three hundred people would have died and many others been stuck with chronic health problems.”

4. How big was the sample?

In general, the more participants in a study, the more valid its results. That said, a large sample is sometimes impossible or even undesirable for certain kinds of studies. During COVID-19, limited time has constrained the sample sizes.

However, that acknowledged, it’s still the case that some studies have been much larger than others—and the sample sizes of the vaccine trials can still provide us with enough information to make informed decisions. Doctors and nurses on the front lines of COVID-19—who are now the very first people being injected with the vaccine—think in terms of “biological plausibility,” as Hass says.

Did the admittedly rushed FDA approval of the Pfizer-BioNTech vaccine make sense, given what we already know? Tens of thousands of doctors who have been grappling with COVID-19 are voting with their arms, in effect volunteering to be a sample for their patients. If they didn’t think the vaccine was safe, you can bet they’d resist it. When the vaccine becomes available to ordinary people, we’ll know a lot more about its effects than we do today, thanks to health care providers paving the way.

5. Did the researchers control for key differences, and do those differences apply to you?

Diversity or gender balance aren’t necessarily virtues in experimental research, though ideally a study sample is as representative of the overall population as possible. However, many studies use intentionally homogenous groups, because this allows the researchers to limit the number of different factors that might affect the result.

While good researchers try to compare apples to apples, and control for as many differences as possible in their analyses, running a study always involves trade-offs between what can be accomplished as a function of study design, and how generalizable the findings can be.

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You also need to ask if the specific population studied even applies to you. For example, when one study found that cloth masks didn’t work in “high-risk situations,” it was sometimes used as evidence against mask mandates.

However, a look beyond the headlines revealed that the study was of health care workers treating COVID-19 patients, which is a vastly more dangerous situation than, say, going to the grocery store. Doctors who must intubate patients can end up being splattered with saliva. In that circumstance, one cloth mask won’t cut it. They also need an N95, a face shield, two layers of gloves, and two layers of gown. For the rest of us in ordinary life, masks do greatly reduce community spread, if as many people as possible are wearing them.

6. Was there a control group?

One of the first things to look for in methodology is whether the population tested was randomly selected, whether there was a control group, and whether people were randomly assigned to either group without knowing which one they were in. This is especially important if a study aims to suggest that a certain experience or treatment might actually cause a specific outcome, rather than just reporting a correlation between two variables (see next point).

For example, were some people randomly assigned a specific meditation practice while others engaged in a comparable activity or exercise? If the sample is large enough, randomized trials can produce solid conclusions. But, sometimes, a study will not have a control group because it’s ethically impossible. We can’t, for example, let sick people go untreated just to see what would happen. Biomedical research often makes use of standard “treatment as usual” or placebos in control groups. They also follow careful ethical guidelines to protect patients from both maltreatment and being deprived necessary treatment. When you’re reading about studies of masks, social distancing, and treatments during the COVID-19, you can partially gauge the reliability and validity of the study by first checking if it had a control group. If it didn’t, the findings should be taken as preliminary.

7. Did the researchers establish causality, correlation, dependence, or some other kind of relationship?

We often hear “Correlation is not causation” shouted as a kind of battle cry, to try to discredit a study. But correlation—the degree to which two or more measurements seem connected—is important, and can be a step toward eventually finding causation—that is, establishing a change in one variable directly triggers a change in another. Until then, however, there is no way to ascertain the direction of a correlational relationship (does A change B, or does B change A), or to eliminate the possibility that a third, unmeasured factor is behind the pattern of both variables without further analysis.

In the end, the important thing is to accurately identify the relationship. This has been crucial in understanding steps to counter the spread of COVID-19 like shelter-in-place orders. Just showing that greater compliance with shelter-in-place mandates was associated with lower hospitalization rates is not as conclusive as showing that one community that enacted shelter-in-place mandates had lower hospitalization rates than a different community of similar size and population density that elected not to do so.

We are not the first people to face an infection without understanding the relationships between factors that would lead to more of it. During the bubonic plague, cities would order rodents killed to control infection. They were onto something: Fleas that lived on rodents were indeed responsible. But then human cases would skyrocket.

Why? Because the fleas would migrate off the rodent corpses onto humans, which would worsen infection. Rodent control only reduces bubonic plague if it’s done proactively; once the outbreak starts, killing rats can actually make it worse. Similarly, we can’t jump to conclusions during the COVID-19 pandemic when we see correlations.

8. Are journalists and politicians, or even scientists, overstating the result?

Language that suggests a fact is “proven” by one study or which promotes one solution for all people is most likely overstating the case. Sweeping generalizations of any kind often indicate a lack of humility that should be a red flag to readers. A study may very well “suggest” a certain conclusion but it rarely, if ever, “proves” it.

This is why we use a lot of cautious, hedging language in Greater Good , like “might” or “implies.” This applies to COVID-19 as well. In fact, this understanding could save your life.

When President Trump touted the advantages of hydroxychloroquine as a way to prevent and treat COVID-19, he was dramatically overstating the results of one observational study. Later studies with control groups showed that it did not work—and, in fact, it didn’t work as a preventative for President Trump and others in the White House who contracted COVID-19. Most survived that outbreak, but hydroxychloroquine was not one of the treatments that saved their lives. This example demonstrates how misleading and even harmful overstated results can be, in a global pandemic.

9. Is there any conflict of interest suggested by the funding or the researchers’ affiliations?

A 2015 study found that you could drink lots of sugary beverages without fear of getting fat, as long as you exercised. The funder? Coca Cola, which eagerly promoted the results. This doesn’t mean the results are wrong. But it does suggest you should seek a second opinion : Has anyone else studied the effects of sugary drinks on obesity? What did they find?

It’s possible to take this insight too far. Conspiracy theorists have suggested that “Big Pharma” invented COVID-19 for the purpose of selling vaccines. Thus, we should not trust their own trials showing that the vaccine is safe and effective.

But, in addition to the fact that there is no compelling investigative evidence that pharmaceutical companies created the virus, we need to bear in mind that their trials didn’t unfold in a vacuum. Clinical trials were rigorously monitored and independently reviewed by third-party entities like the World Health Organization and government organizations around the world, like the FDA in the United States.

Does that completely eliminate any risk? Absolutely not. It does mean, however, that conflicts of interest are being very closely monitored by many, many expert eyes. This greatly reduces the probability and potential corruptive influence of conflicts of interest.

10. Do the authors reference preceding findings and original sources?

The scientific method is based on iterative progress, and grounded in coordinating discoveries over time. Researchers study what others have done and use prior findings to guide their own study approaches; every study builds on generations of precedent, and every scientist expects their own discoveries to be usurped by more sophisticated future work. In the study you are reading, do the researchers adequately describe and acknowledge earlier findings, or other key contributions from other fields or disciplines that inform aspects of the research, or the way that they interpret their results?

Greater Good’s Guide to Well-Being During Coronavirus

Practices, resources, and articles for individuals, parents, and educators facing COVID-19

This was crucial for the debates that have raged around mask mandates and social distancing. We already knew quite a bit about the efficacy of both in preventing infections, informed by centuries of practical experience and research.

When COVID-19 hit American shores, researchers and doctors did not question the necessity of masks in clinical settings. Here’s what we didn’t know: What kinds of masks would work best for the general public, who should wear them, when should we wear them, were there enough masks to go around, and could we get enough people to adopt best mask practices to make a difference in the specific context of COVID-19 ?

Over time, after a period of confusion and contradictory evidence, those questions have been answered . The very few studies that have suggested masks don’t work in stopping COVID-19 have almost all failed to account for other work on preventing the disease, and had results that simply didn’t hold up. Some were even retracted .

So, when someone shares a coronavirus study with you, it’s important to check the date. The implications of studies published early in the pandemic might be more limited and less conclusive than those published later, because the later studies could lean on and learn from previously published work. Which leads us to the next question you should ask in hearing about coronavirus research…

11. Do researchers, journalists, and politicians acknowledge limitations and entertain alternative explanations?

Is the study focused on only one side of the story or one interpretation of the data? Has it failed to consider or refute alternative explanations? Do they demonstrate awareness of which questions are answered and which aren’t by their methods? Do the journalists and politicians communicating the study know and understand these limitations?

When the Annals of Internal Medicine published a Danish study last month on the efficacy of cloth masks, some suggested that it showed masks “make no difference” against COVID-19.

The study was a good one by the standards spelled out in this article. The researchers and the journal were both credible, the study was randomized and controlled, and the sample size (4,862 people) was fairly large. Even better, the scientists went out of their way to acknowledge the limits of their work: “Inconclusive results, missing data, variable adherence, patient-reported findings on home tests, no blinding, and no assessment of whether masks could decrease disease transmission from mask wearers to others.”

Unfortunately, their scientific integrity was not reflected in the ways the study was used by some journalists, politicians, and people on social media. The study did not show that masks were useless. What it did show—and what it was designed to find out—was how much protection masks offered to the wearer under the conditions at the time in Denmark. In fact, the amount of protection for the wearer was not large, but that’s not the whole picture: We don’t wear masks mainly to protect ourselves, but to protect others from infection. Public-health recommendations have stressed that everyone needs to wear a mask to slow the spread of infection.

“We get vaccinated for the greater good, not just to protect ourselves ”

As the authors write in the paper, we need to look to other research to understand the context for their narrow results. In an editorial accompanying the paper in Annals of Internal Medicine , the editors argue that the results, together with existing data in support of masks, “should motivate widespread mask wearing to protect our communities and thereby ourselves.”

Something similar can be said of the new vaccine. “We get vaccinated for the greater good, not just to protect ourselves,” says Hass. “Being vaccinated prevents other people from getting sick. We get vaccinated for the more vulnerable in our community in addition for ourselves.”

Ultimately, the approach we should take to all new studies is a curious but skeptical one. We should take it all seriously and we should take it all with a grain of salt. You can judge a study against your experience, but you need to remember that your experience creates bias. You should try to cultivate humility, doubt, and patience. You might not always succeed; when you fail, try to admit fault and forgive yourself.

Above all, we need to try to remember that science is a process, and that conclusions always raise more questions for us to answer. That doesn’t mean we never have answers; we do. As the pandemic rages and the scientific process unfolds, we as individuals need to make the best decisions we can, with the information we have.

This article was revised and updated from a piece published by Greater Good in 2015, “ 10 Questions to Ask About Scientific Studies .”

About the Authors

Jeremy Adam Smith

Uc berkeley.

Jeremy Adam Smith edits the GGSC’s online magazine, Greater Good . He is also the author or coeditor of five books, including The Daddy Shift , Are We Born Racist? , and (most recently) The Gratitude Project: How the Science of Thankfulness Can Rewire Our Brains for Resilience, Optimism, and the Greater Good . Before joining the GGSC, Jeremy was a John S. Knight Journalism Fellow at Stanford University.

Emiliana R. Simon-Thomas

Emiliana R. Simon-Thomas, Ph.D. , is the science director of the Greater Good Science Center, where she directs the GGSC’s research fellowship program and serves as a co-instructor of its Science of Happiness and Science of Happiness at Work online courses.

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Gender, immunological response, and covid-19: an assessment of vaccine strategies in a pandemic region of oaxaca, méxico.

1. Introduction

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Click here to enlarge figure

Variable	Total Samples		Positive Samples
Variable	n	%	n	%
Gender
Females	88	58.7	83	94.3
Males	62	41.3	56	90.3
Type of vaccine
CanSino	118	78.6	107	90.6
AstraZeneca	16	10.6	16	100
Others	16	10.6	16	100
Age group
18–30 years old	30	20.0	30	100
31–45 years old	29	19.3	25	86.0
46–59 years old	27	18.0	25	92.5
60–69 years old	31	20.7	28	90.3
70 years and older	33	22.0	31	93.9
Comorbidity
Absence	104	69.4	97	93.2
≥1	46	30.6	42	91.3
BMI
Healthy weight	26	29.9	24	92.3
Overweight	35	40.2	35	100
Obesity	26	29.9	23	88.5

Variable	No. of Positive Individuals	Antibody Rate %	95% ICs
Gender
Female	83	60	51–67
Male	56	40	32–48
Age group
18–30 years old	30	22	15–29
31–45 years old	25	18	12–25
46–59 years old	25	18	12–25
60–69 years old	28	20	14–27
70 years and older	31	22	16–29
BMI
Healthy weight	24	29	20–39
Overweight	35	42	32–53
Obesity	23	28	19–38

	-value
Gender	−2.21	0.028
Comorbidity	1.23	0.220
	-value
Type of vaccine	0.73	0.483
Age group	0.36	0.839
Body mass index *	1.65	0.198

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Share and Cite

Rodríguez-Martínez, L.M.; Chavelas-Reyes, J.L.; Medina-Ramírez, C.F.; Cabrera-Santos, F.J.; Fernández-Santos, N.A.; Aguilar-Durán, J.A.; Pérez-Tapia, S.M.; Rodríguez-González, J.G.; Rodríguez Pérez, M.A. Gender, Immunological Response, and COVID-19: An Assessment of Vaccine Strategies in a Pandemic Region of Oaxaca, México. Microbiol. Res. 2024 , 15 , 1007-1015. https://doi.org/10.3390/microbiolres15020066

Rodríguez-Martínez LM, Chavelas-Reyes JL, Medina-Ramírez CF, Cabrera-Santos FJ, Fernández-Santos NA, Aguilar-Durán JA, Pérez-Tapia SM, Rodríguez-González JG, Rodríguez Pérez MA. Gender, Immunological Response, and COVID-19: An Assessment of Vaccine Strategies in a Pandemic Region of Oaxaca, México. Microbiology Research . 2024; 15(2):1007-1015. https://doi.org/10.3390/microbiolres15020066

Rodríguez-Martínez, Luis M., José L. Chavelas-Reyes, Carlo F. Medina-Ramírez, Francisco J. Cabrera-Santos, Nadia A. Fernández-Santos, Jesús A. Aguilar-Durán, Sonia M. Pérez-Tapia, Josefina G. Rodríguez-González, and Mario A. Rodríguez Pérez. 2024. "Gender, Immunological Response, and COVID-19: An Assessment of Vaccine Strategies in a Pandemic Region of Oaxaca, México" Microbiology Research 15, no. 2: 1007-1015. https://doi.org/10.3390/microbiolres15020066

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Open Access

Peer-reviewed

Research Article

Exploring barriers to care home research recruitment during the COVID-19 pandemic: The influence of social media recruitment posts and public sentiment

Contributed equally to this work with: Mariyana Schoultz, Claire Mcgrogan

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Writing – original draft

* E-mail: [email protected]

Affiliation School of Health and Life Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Roles Formal analysis, Writing – original draft, Writing – review & editing

¶ ‡ These authors also contributed equally to this work

Affiliation Centre for Rural Health Sciences, University of the Highlands and Islands, Inverness, United Kingdom

Roles Formal analysis, Validation, Writing – review & editing

Affiliation Nursing Studies, School of Health in Social Sciences, The University of Edinburgh, Edinburgh, United Kingdom

Mariyana Schoultz,
Claire Mcgrogan,
Clare Carolan,
Leah Macaden,
Michelle Beattie

Published: June 21, 2024
https://doi.org/10.1371/journal.pone.0303609
Peer Review
Reader Comments

Introduction

Recruitment of care home staff to research studies is recognised as challenging. This was further exacerbated by the COVID-19 pandemic and the associated negative media portrayal of care home workers. Social media use has surged since the onset of COVID-19 lockdowns, offering a plausible approach to understanding the barriers to care home research recruitment and gaining insight into public perceptions of care home workers.

To utilise comments from two Facebook recruitment posts to: 1) gain an understanding of potential barriers to recruitment of healthcare workers (HCWs) in UK care homes, and 2) explore public sentiment towards care home research and care homes in the context of the COVID-19 pandemic.

This cross-sectional study analysed comments from two Facebook posts (available June-October 2021) advertising a separate study on psychological support for care staff during the pandemic. This study was situated within a larger investigation into the mental health and wellbeing of care home staff and employed both qualitative analysis and quantitative methods (word count and correlations between words used and between posts).

Three themes were identified from the qualitative analysis: support, mistrust and blame. There was a greater use of words associated with support and negative emotive words in post 2. Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1 which indicated a resentful sentiment from those who advocate freedom of choice and control. Discussion of mistrust towards researchers was most prominent in post 1 indicating the importance of relationship building between researchers and HCWs in UK care homes. With attribution to blame, there was a larger range of negative emotion words than positive emotion words.

Discussion and conclusion

Taken together our findings offer novel insights into why recruitment to care home research during the pandemic including the use of social media might be problematic. Social media is a useful tool for recruitment but should not be considered as a one-time input. Researchers should pro-actively engage with the study population from the start using co-design with resident and public groups to support recruitment and ensure these populations are accurately represented within research.

Citation: Schoultz M, Mcgrogan C, Carolan C, Macaden L, Beattie M (2024) Exploring barriers to care home research recruitment during the COVID-19 pandemic: The influence of social media recruitment posts and public sentiment. PLoS ONE 19(6): e0303609. https://doi.org/10.1371/journal.pone.0303609

Editor: Ali B. Mahmoud, St John’s University, UNITED STATES

Received: July 22, 2022; Accepted: April 28, 2024; Published: June 21, 2024

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

Data Availability: Data are available from OSF at https://osf.io/f7sae/ .

Funding: This study was funded by RCN Foundation. The award was received by the lead author. The funder did not play any role in the study design,data collection and analysis, decision to publish, or preparation of the manuscript.

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

Since 2020, the COVID-19 pandemic has impacted the lives of people across the globe. The pandemic presented several challenges for health care workers (HCWs) in nursing and care home settings who experienced significant adjustments to their working environments as well as an increase in residents’ mortality [ 1 ]. Additionally, HCWs risked their own health and that of their families while caring for residents with COVID-19 [ 1 ]. The Health Belief Model (HBM) offers a comprehensive framework to comprehend the responses of HCWs in nursing and care homes amid the COVID-19 pandemic. In this context, HCWs’ perception of the threat posed by the virus, both to their physical well-being and mental health, aligns with the core tenets of the HBM. Studies indicate that HCWs perceived a considerable threat, not only to their physical well-being but also to their mental health, as evidenced by high levels of depression and anxiety [ 2 – 5 ]. Additionally, the perceived lack of support from the UK national governments further heightened the sense of vulnerability among HCWs, with a substantial proportion considering leaving their professions due to the inadequacies of support systems [ 6 ].

The negative psychological impact on HCWs was exacerbated by stigmatisation, particularly through negative media portrayals of HCWs during COVID-19, by unfairly blaming them for high numbers of COVID-related deaths and imposing restrictions on visits [ 7 ]. Here, the HBM helps interpret how these external factors contribute to HCWs’ perceptions of susceptibility and severity of the threat posed by the pandemic.

In response to the pandemic and to support all essential workers, Public Health England introduced and recommended a free-to-access online version of Psychological First Aid (PFA) training in June 2020 [ 8 ]. The PFA was originally designed as a brief training course to help those involved in disasters and/or traumatic events to reduce their initial distress and enhance their long-term coping [ 9 ]. Some evidence suggests that PFA might be useful for HCWs [ 10 ]. However, another systematic review of usefulness of PFA for HCWs found that there was a lack of empirical evidence, and many recommendations were based on expert opinions [ 11 ]. Taken together, the need to derive tailored evidence-based psychological support to HCWs is pressing. Our study into PFA for HCWs prompted this work due to challenges with recruitment and the perceived possibilities of social media.

Several barriers to research within care homes are well established and have been amplified by the pandemic [ 12 ]. Notably, recruitment of care home staff to research studies is recognised as challenging [ 13 ]. There could be several reasons for this. Firstly, the impact of COVID-19 likely exacerbated this issue with increased workload, reduction in staff, and subsequent reports of exhaustion among the HCWs. Secondly, the impact of the negative media portrayal of care home staff during the pandemic may have increased their reluctance to participate in research [ 12 ]. Thirdly, limited communication pathways to inform care homes about research opportunities can lack clarity. For example, access to work email or work computers by HCWs has been reported as problematic and therefore research information is difficult to distribute [ 14 ]. Despite this, it is important for care homes to have opportunities to participate in research, not only as a means to inform future research but as a direct benefit to HCWs by improving their well-being, socialisation and providing a therapeutic benefit for them, as well as opportunities for their voices to be heard [ 15 , 16 ]. Thus, using different approaches and strategies to engage them in research is imperative in addition to some UK initiatives such as ENRICH (ENabling Research In Care Homes) network [ 17 ].

Attempts to minimise the spread of COVID-19 resulted in worldwide lockdowns with social media becoming the preferred means of interaction within the public [ 18 ]. Hence, the expanding the use of social media provides an alternative approach to attract and recruit research participants. Facebook has the largest social media following with 2.3 billion users worldwide and has been used previously to gain perspectives of hard to reach groups [ 19 – 21 ]. In this paper, we are reporting on the responses to a Facebook recruitment advert for a national cross-sectional survey of UK care home HCWs conducted between June and October 2021 to provide insight into potential barriers to recruitment. Given that the recruitment advert was accessible to the general population, we believed that understanding the public’s perception of the proposed research study and more generally care homes during the pandemic, might usefully inform future recruitment from care and nursing homes and psychological research and intervention development for that population. Thus, our aims, informed by the HBM, were to: 1) gain an understanding of potential barriers to recruitment of HCWs in UK care homes, and 2) explore the public sentiment towards care home research and care homes in the context of the COVID-19 pandemic.

Materials and methods

Study design.

A cross-sectional retrospective observational review of comments made by the public to specific social media recruitment posts (n = 2) was undertaken. This secondary data approach was chosen to capture the public perceptions as it allowed for unfiltered opinions. It also avoided social desirability biases such as the Hawthorne effect where participants may change their behaviour, or censor their opinions, if they are aware they are being studied [ 22 , 23 ]. The data collection and analysis method complied with the terms and conditions of the Facebook data source.

Data collection

The comments were manually extracted on a Microsoft Excel spreadsheet from two Facebook posts advertising a research opportunity for a study on psychological support for care staff during the pandemic. Data were stored on the Northumbria University secure server and only the research team had access to it. Post 1 (see Fig 1 ) was a sponsored post from Northumbria University’s official Facebook page and set to reach adults in the UK. The recruitment advert in this post was hosted on the university’s Facebook page and included university branding and reference to the funding organisation. Post 2 (see Fig 2 ) was advertised from a Facebook page created by the author (MS) but not affiliated with Northumbria University, which advertised research relating to the impact of COVID-19 on mental health and quality of life. Both posts were live at the same time between June and October 2021. MS had author access and therefore an opportunity to reply to questions on the Facebook Post 2, while this was not the case with the Facebook Post 1.

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https://doi.org/10.1371/journal.pone.0303609.g001

https://doi.org/10.1371/journal.pone.0303609.g002

Ethical considerations

Ethical approval was granted from the Faculty of Health and Life Sciences at Northumbria University REF:32662NU and consent was waived.

Comments were qualitatively analysed using the 6-steps of thematic analysis defined by Braun and Clarke [ 24 ]. All members of the team familiarised themselves with the data. During phase two and three, comments were independently coded by MS, CM and MB with initial codes inductively generated and then grouped into themes via collaborative discussion. Subsequently, phase four and five followed, where all the research team met and reviewed if the initial themes were supported by the code extracts and the overall data set. Themes were defined and named with the final names of the key themes and sub-themes agreed by consensus. A report (phase 6) on the findings was agreed by all team members.

The quantitative analysis included exploration of word usage. Several key word categories were identified from the findings of the thematic analysis. Use of these word categories were counted and recorded. Singular and plural pronoun use were quantified to gain insight into perceptions of personal responsibility in the context of the pandemic, displacement of responsibility and locus of control. Table 1 displays word categories and word bank. Finally, the number of words used in each comment was recorded.

https://doi.org/10.1371/journal.pone.0303609.t001

A series of t-tests were conducted to compare use of each word category and comment length between posts 1 and 2. Correlations were conducted to explore relationships between the word categories in each post.

199 data points were collected across the two posts, 133 from post 1 and 66 from post 2. These comprised of 90 main comments and linked replies from 94 contributors to post 1, and 26 main comments and linked replies from 38 contributors to post 2. Post 2 reached 14983 people and 20 shares, but we were unable to retrieve this data for post 1.

Three themes were identified from the data: i) support, ii) mistrust, and iii) blame.

I. Support.

Twenty-three data points made reference to support. Most data points (n = 20) relating to this theme highlighted a lack of support for HCWs in care homes and the negative consequences for their well-being as a result. It is a reasonable assumption that if care home staff are physically and mentally exhausted there will be no ‘reserve’ for them to consider participating in research.

“I’m absolutely mentally and physically exhausted , lost a stone in weight with stress , and had no support , myself and staff are done in–Contributor 98 (Post 2) “ … as for support I get more support of my 6-year-old Primark knickers than I do from the care industry”—Contributor 123 (Post 2)

Those that received support suggested this was beneficial. Whilst HCWs reported benefits from support, including their mental health and coping with the demands of care home work, the direct connection between support and research recruitment is largely theoretical i.e. more time/headspace they have, the more opportunity they would have to partake in research.

“ Yes . Co counselling gets rid of stress . Life is then much easier . Can be learned and troubles eased . ”—Contributor 23 (Post 1) “ Yes we did and it was very beneficial to all our staff”—Contributor 101 (Post 2)

II. Mistrust.

Ninety data points were categorised as referencing mistrust, with the majority (n = 55) relating to vaccines, followed by (n = 35) relating to lack of trust towards the government and researchers. Often those working for the Government and researchers were not seen as different by respondents, but rather similarly mistrusted as hierarchies.

Vaccine issues were the most commonly discussed topic within the comments on both posts, with n = 55 data points referencing this subject. Two sub-themes were identified from the data: a) lack of choice, and b) harm.

Participants shared views that emphasised the wariness placed by some about local authorities in relation to COVID-19 and the potential adverse effects and harm of the COVID-19 vaccine which ultimately led to suspicion and mistrust of both government authorities and researchers.

“ I’ve chosen vaccine , but it was my choice”–Contributor 105 (Post 2) “ no one should be forced to vaccinate”—Contributor 105 (Post 2)

Lack of autonomy and coercion were expressed and the consequences of care staff not receiving the vaccination were also discussed in relation to threatened job losses.

“ I definitely don’t want the vaccine but stand to lose a job I’ve done for 30 years and love”–Contributor 96 (Post 2) “ we’re fine—apart from the slight stress caused by being fired because we don’t want the vaccine . ”—Contributor 86 (Post 1) “ Sure also it will help if the authorities stop blackmailing them No vax No job—Contributor 10 (Post 1)

There was also mistrust expressed by a few participants in relation to the risks and potential harm associated with receiving the vaccine and concerns around its efficacy. Those respondents seemed to be influenced by personal experience of the COVID-19 vaccine related harm.

“ my husband’s son-in-law ended up with blood clots and a stroke”–Contributor 18 (Post 1) “ hell , it’s disgusting my friends that have had the dodgy vaccine are dropping like flies one friend had a stroke the day after . So Evil . ”–Contributor 90 (Post 1) “ Do these jabs work ? No . Two of my friends had jabs sadly one died of COVID . Other people tell me the same , they had jab one still got COVID . The lady I knew had the two jabs yet still got caught COVID and passed away . ”–Contributor 18 (Post 1)

On occasions the effects of mistrust led to calls for vengeance.

“The Government and Media should face a trial over this fake PLANDEMIC … . and should face charges of Crimes against Humanity .. then Executed!!!!”–Contributor 94 (Post 1) “More criminality by government lackies”–Contributor 43 (Post 1)

Mistrust was also expressed towards the researchers conducting the study, academic institutions and research more widely. This included scepticism towards the evidence and wasteful tax expenditure.

“Think a bit late Northumbria University . I don’t buy your doing this survey to find out if care workers had phycological support . I think the government wants to know what carers are thinking and feeling right now about being blackmailed and victimised to getting vaccinated to keep their jobs . "–Contributor 17 (Post 1) “Someone somewhere wants to create another ology to make more money out of the taxpayer.”–Contributor 20 (Post 1) “97% of all scientists agree with the people funding them”–Contributor 26 (Post 1)

Multiple comments (n = 9) also served to minimise the impact or severity of COVID-19 suggesting a mistrust of the scientific evidence being presented and a disbelief that there was a pandemic

“How many more masks do you want to wear for a flu with a survival rate of 99 . 9%?”–Contributor 95 (Post 1) “What pandemic?”–Contributor 42 (Post 1) “There is no pandemic”—Contributor 46 (Post 1) “Ask sage they’ll know best”–Contributor 16 (Post 1)

III. Blame.

Fifteen data points referred to blame. Blame was palpable at multiple levels (care home staff, NHS, government and academia) and, on occasions, were extreme and expressed as seeking vengeance. Further blame comments referred to the role of the government and NHS. The poor relationship between NHS and care home staff was also exemplified.

“Our NHS colleagues came into our home and called us murderers for losing as many residents , even though it was them that placed an untested patient into the home , because at the time it wasn’t government guidelines , and thus caused the virus to spread in our home , a dementia unit with no chance at all of social distancing , isolation nothing , but we get the Flack and criticism and our NHS colleagues get the praise , I’m sorry but local NHS trust look down their noses at care staff in homes”–Contributor 111 (Post 2) “This happened after the government working in tandem with the NHS introduced INHUMANE TORTURE TECHNIQUES . "–Contributor 64 (Post 1)

Blame was also expressed towards managers and the government in relation to lack of adequate funding and resources.

“The reason the government are increasing the NI contributions is to keep these inept useless managers , team leaders , social workers , managers and their managers in care homes in jobs . They are bleeding the system dry . ”–Contributor 17 (Post 1) “That is the truth ! CEO’s and directors will find that a lot of expenditure is spent on middle management , when a better strategy would be to employ competent people who can log directly into a system of work . 70-80k per annum is a lot of money to pay one individual unnecessarily when people/ the workforce have access to use simple technology . ”–Contributor 62 (Post 1)

There were a few extreme views of public sentiment seeking vengeance towards care home staff for their role as enabling and allowing residents to be vaccinated.

“ The carers that were complicit to watching residents die of loneliness and watching the results of vaccinating our elderly should have no choice as the residence had no choice . What goes around comes around” . –Contributor 18 (Post 1) “ I have sympathy for those having to take the jag but if you’re a carer who watched what they did to the elderly then yes , it’s karma . You all should have stood up when the rest of us were on the streets telling you all you were being conned . ”–Contributor 37 (Post 1)

Word count analysis

Table 2 displays descriptive statistics and t-test results for the word count analysis.

https://doi.org/10.1371/journal.pone.0303609.t002

Comments on post 2 ( M = 1.33, SD = 2.14) featured significantly more first-person singular pronouns than the comments on post 1 ( M = 0.20, SD = 0.65), t( 197 ) = -5.60, p < .001.

Post 2 comments also included significantly more negative emotion words (Post 2 M = 0.45, SD = 0.73, Post 1 M = 0.20, SD = 0.44), t( 197 ) = -3.03, p < .001, words associated with support (Post 2 M = 0.21, SD = 0.45, Post 1 M = 0.06, SD = 0.24), t( 197 ) = -3.13, p < .001, and words associated with choice (Post 2 M = 0.32, SD = 0.64, Post 1 M = 0.09, SD = 0.34) t( 197 ) = -3.31, p < .001, than comments on post 1.

Correlations.

Table 3 displays correlations between word categories in posts 1 and 2.

https://doi.org/10.1371/journal.pone.0303609.t003

Use of positive emotion words was positively correlated with use of first-person singular pronouns in both posts (Post 1 r = .19, p = .03, Post 2 r = .31, p = .01), first-person plural pronouns in post 2 ( r = .31, p = .01), and second-person pronouns in post 1 ( r = .22, p < .001).

Use of negative emotion words was positively correlated with use of first-person singular pronouns ( r = .36, p < .001), first-person plural pronouns ( r = .35, p < .001), and third-person plural pronouns ( r = .36, p < .001) in post 2. Negative emotion word use also positively correlated as follows: with words associated with support ( r = .27, p = .03); the COVID-19 pandemic ( r = .38, p < .001) in post 2; and words associated with vaccines in post 1 ( r = .18, p = .04).

Use of words associated with vaccines positively correlated with first-person singular pronouns in both posts (post 1 r = .32, p < .001, post 2 r = .44, p < .001), and first-person plural pronouns ( r = .33, p < .001), and second-person pronouns ( r = .47, p = .03) in post 2. Vaccine words also positively correlated with words associated with choice in post 2 ( r = .48, p < .001) and the COVID-19 pandemic in post 1 ( r = .44, p < .001).

Words associated with support positively correlated with first-person singular pronouns ( r = .29, p = .02) in post 2. Choice words were positively correlated with first-person singular pronouns ( r = .26, p = .04) and second-person pronouns ( r = .30, p = .01) in post 2. Words associated with the COVID-19 pandemic were also positively correlated with first-person singular pronouns ( r = .18, p = .04) in post 1.

This paper aimed to gain understanding of potential barriers to recruitment of HCWs in UK care homes and explore public sentiments towards research in care homes during the Covid-19 pandemic by analysing comments on social media recruitment posts for a study of HCWs’ well-being. While our analysis primarily focused on themes of support, mistrust, and blame, integrating insights from the Health Belief Model (HBM) enriches our understanding of the underlying mechanisms driving these sentiments. There was a greater use of words associated with support and negative emotive words in post 2. Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1. Discussion of mistrust towards researchers was most prominent in Facebook post 1. With attribution to blame, there was a larger range of negative emotion words than positive emotion words. While the recruitment strategy using social media for the wider study was successful, it was not without challenges. Thus, taken together, our findings offer some insights into why recruitment to care home studies during the pandemic, using social media may have been challenging based on the sentiments shared both by HCWs and by the public.

Our findings underscore the crucial role of support, encompassing both psychological and logistical assistance, in mitigating the adverse effects of the pandemic on care home staff’s well-being. The Health Belief Model provides a valuable framework for interpreting these findings, particularly regarding perceptions of susceptibility, severity, and perceived benefits of seeking support. Perceived susceptibility to negative outcomes, such as mental health challenges, may motivate individuals to seek support services. However, our study suggests that care home staff received little psychological support during the pandemic, aligning with previous research demonstrating low uptake of well-being interventions among HCWs [ 11 , 25 ]. This perceived lack of support may exacerbate feelings of vulnerability and contribute to increased depression, anxiety, and insomnia [ 26 – 28 ]. The positive relationship between words associated with support and negative emotive words in our word count analysis further underscores the importance of addressing support deficits to alleviate psychological distress among care home staff. Clearly, care home staff’s physiological and safety needs would require to be met before they could participate in research. Meeting these fundamental needs are important considerations when recruiting to care home studies. Researchers must ensure physical comfort is obtained from adequate breaks, minimal participant burden and creating an environment for psychological safety i.e., confidentiality etc. Importantly, ensuring care home staff have an opportunity to participate in research has important ethical principle i.e. fairness and equity of opportunity.

Mistrust was a prevalent theme throughout the data, particularly in relation to the effectiveness and risks of the COVID-19 vaccine, authorities, and researchers. Public and care home staff mistrust is important as it can affect all levels of trust in societal and social systems [ 29 ]. For example, mistrust in authorities can also become mistrust in researchers. Trust depends on people’s previous experiences, with negative experiences being more powerful at building mistrust, than positive experiences building trust [ 30 ]. Therefore, resources need to be directed towards creating lots of positive experiences with care home workers to build trust, and subsequently increase their likelihood of engaging with research projects. Additionally, how trust is perceived is dependent upon individuals’ perceptions of what is fair [ 31 ]. Many respondents in this study felt that it was unfair to enforce vaccinations for care home staff, as this removed individual autonomy. Some posts even suggested that care home staff should be vaccinated in retribution for allowing elderly residents to be vaccinated. To understand care home workers values there is a need for researchers to build relationships with them.

Additionally, there were intense views around the efficacy and safety of the COVID-19 vaccine with some reporting serious adverse medication events from people known to them. Of course, these could be extreme views by those who have had recent negative experiences, otherwise known as proximity fallacy. These posts contained a discussion about vaccine and related choices and consequences, despite no mention of vaccine in the recruitment post. However, the timing of the recruitment post coincided with public health and government discussions and activities around the vaccine in general and the compulsory vaccination for HCWs, placing the responsibility to protect vulnerable people (care home community) on HCWs (individual level). Thus, it is not surprising to see vaccine and choice discussion on the posts.

Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1. In addition, post 2 had stronger associations between vaccine words and choice words or positive emotion or singular pronouns in comparison to post 1. It is possible to assume that the people commenting on post 2 felt they had more choice or more choice should be available to people regarding the vaccine, but also could indicate a higher internal locus of control where they have a personal responsibility around limiting the transmission of the disease. This fits with the theory of locus of control which looks at the extent to which people believe they control their lives (internal locus) or if others control their life, such as the influence and power of government or other forces (external locus) [ 32 ]. This could be particularly important in the context of the global pandemic and the mental health of the public and HCWs. Having a higher external locus can exacerbate negative feelings, feelings of threat and having depressive thoughts in comparison to having a higher internal locus where people feel more in control and that their actions matter [ 33 , 34 ]. What this means for barriers to recruitment is that those that feel they have less control over their life are more likely to have mistrust towards government and researchers and are therefore less likely to participate in research. Thus, as mentioned before, doing work that can increase trust in researchers by building relationships with care homes, can change the perception HCWs have towards researchers.

Findings relating to mistrust of researchers and perceptions of government sponsorship highlight the need for better collaboration and sharing of power between researchers and HCWs. All stages of the research process would benefit from co-design, particularly preparation of recruitment and data collection materials to ensure they appeal to and are suitable for the population. Researchers should be aware of the potential impact of including institutional and funders’ information on recruitment posts. Discussion of mistrust towards researchers was most prominent on post Facebook 1. Given the clear need for more research to be conducted with HCWs, it is important that as researchers we work to foster relationships with this population–research champions to promote collaboration with care and nursing homes. This also highlights the importance of researcher responses to comments–where recruitment posts attract some negative comments this may ‘taint’ the post and put off people who might otherwise have considered taking part. Researchers engaging with the comments to provide and clarify information about the study (as would happen face to face) might help to limit this and increase the credibility of the post.

Finally, a preponderance of negative emotion words was apparent and attribution of blame in relation to care home staff was evident within our findings. Applying the Health Belief Model, individuals’ perceptions of blame may reflect their beliefs about the causes and controllability of adverse outcomes. There was a larger range of negative emotion words than positive emotion words. Negative emotion words were more likely to be higher valency e.g., depressed, trauma, suffering, than the positive emotion words which were typically lower valence e.g., funny, glad, happy. This suggests negative emotions experienced and expressed were more intense and warrants the need for development and accessibility of public health interventions that can address these intense negative emotions. Use of negative emotions words in the comments on post 1 were typically used in reference to others e.g., care home residents being lonely, whereas in post 2 they were more often used in reference to care home staff being burnt out and exhausted. This further demonstrates the dominant external locus of control among the participants, but also highlights the general worry about the vulnerable and the underappreciated members of the public and dissatisfaction about how the challenges of the pandemic were handled.

Strengths and limitations

It is important to acknowledge the strengths and limitations of using social media such as Facebook data as a research source. One of the strengths is that is a quick and easy way of accessing public’s views on specific topics without needing an explicit consent since the information is in public domain [ 35 ]. Further, as comments were generated naturalistically, we can assume that opinions expressed are uncensored and not subject to the demand characteristics and social desirability bias often observed within the context of experimental designs. However, this method of data collection also presents notable limitations. Firstly, demographic information could not be collected for those who contributed to the comments. It was unclear from some posts whether these were a lay public perspective or specifically experiences of care home workers. While it can be inferred that contributors to the comments on post 2 were more likely to be HCWs due to the content of the comments and the target audience of the page, similar inferences cannot be made for post 1. Secondly, difficulties arose with determining which were genuine opinions and experiences and which comments were posted to be deliberately controversial or confrontational (particularly in the context of a sensitive and potentially divisive topic such as the COVID-19 pandemic). As such, all comments were extracted and treated as valid data points, however this may mean that excessively negative, anti-vaccine or anti-pandemic opinions were over-represented in the data.

Also, word count analysis does not consider the semantics of the word in the context of the sentence [ 36 ]. Many of the positive emotive words were used to convey sarcasm or irony. Contributors to social media discussions may represent a self-selected group with particular views, potentially skewing the data towards certain sentiments and limiting the generalisability of the findings [ 37 ]. Finally, we want to acknowledge that the method used is insufficient to address aim 1 entirely and future qualitative studies should be undertaken to explore this further.

The negative media portrayal of care homes during the COVID-19 pandemic can be attributed to several interconnected factors [ 38 ]. Heightened mortality rates within care homes, often resulting from the virulent nature of the virus, have become focal points in media coverage, creating a perception of inadequacy in crisis management. Reports on insufficient protective measures, including shortages of essential equipment, and staffing challenges, such as shortages and overwork, contribute to the negative image [ 39 ]. Communication issues, both within care homes and between stakeholders, are highlighted, further impacting the perception of care home efficacy. Public and governmental responses, including policies and financial support, are scrutinized, adding another layer to the narrative. Stigmatization of care homes as COVID-19 hotspots and potential sensationalism by the media contribute to a nuanced and often negative portrayal. This complex interplay of factors underscores the multifaceted nature of the media’s influence on public perception during the pandemic.

Social media is a useful tool for recruitment but should not be considered as a one–off strategy for recruitment but rather built in as an integral avenue for recruitment to research with appropriate ethics approval especially with hard-to-reach groups. Researchers should aim to engage with their target populations to provide as much information about the work as possible and help address misconceptions in the comments which may ‘taint’ the post. Also, useful for researchers working with the HCW population, is to engage in co-design from the start and include PPI groups in recruitment. The implementation of research champions in these settings would benefit care home staff to be proactive in building relationships with researchers and aid recruitment to ensure these populations are properly represented within research.

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Open access
Published: 20 June 2024

Governance of the wildlife trade and the prevention of emerging zoonoses: a mixed methods network analysis of transnational organisations, silos, and power dynamics

Chloe Clifford Astbury 1 , 2 , 3 ,
Anastassia Demeshko 1 ,
Eduardo Gallo-Cajiao 4 , 5 ,
Ryan McLeod 1 ,
Mary Wiktorowicz 1 , 2 , 6 ,
Cécile Aenishaenslin 7 , 8 ,
Katherine Cullerton 9 ,
Kirsten M. Lee 1 , 2 ,
Arne Ruckert 10 ,
A. M. Viens 1 , 3 ,
Peter Tsasis 6 &
Tarra L. Penney 1 , 2 , 3

Globalization and Health volume 20 , Article number: 49 ( 2024 ) Cite this article

Metrics details

Introduction

The wildlife trade is an important arena for intervention in the prevention of emerging zoonoses, and leading organisations have advocated for more collaborative, multi-sectoral approaches to governance in this area. The aim of this study is to characterise the structure and function of the network of transnational organisations that interact around the governance of wildlife trade for the prevention of emerging zoonoses, and to assess these network characteristics in terms of how they might support or undermine progress on these issues.

This study used a mixed methods social network analysis of transnational organisations. Data were collected between May 2021 and September 2022. Participants were representatives of transnational organisations involved in the governance of wildlife trade and the prevention of emerging zoonoses. An initial seed sample of participants was purposively recruited through professional networks, and snowball sampling was used to identify additional participants. Quantitative data were collected through an online network survey. Measures of centrality (degree, closeness, and betweenness) were calculated and the network’s largest clique was identified and characterised. To understand the extent to which organisations were connected across sectors, homophily by sector was assessed using exponential random graph modelling. Qualitative data were collected through semi-structured interviews. The findings from the quantitative analysis informed the focus of the qualitative analysis. Qualitative data were explored using thematic analysis.

Thirty-seven participants completed the network survey and 17 key informants participated in semi-structured interviews. A total of 69 organisations were identified as belonging to this network. Organisations spanned the animal, human, and environmental health sectors, among others including trade, food and agriculture, and crime. Organisation types included inter-governmental organisations, non-governmental organisations, treaty secretariats, research institutions, and network organisations. Participants emphasised the highly inter-sectoral nature of this topic and the importance of inter-sectoral work, and connections were present across existing sectors. However, there were many barriers to effective interaction, particularly conflicting goals and agendas. Power dynamics also shaped relationships between actors, with the human health sector seen as better resourced and more influential, despite having historically lower engagement than the environmental and animal health sectors around the wildlife trade and its role in emerging zoonoses.

The network of transnational organisations focused on the governance of wildlife trade and the prevention of emerging zoonoses is highly multi-sectoral, but despite progress catalysed by the COVID-19 pandemic, barriers still exist for inter-sectoral interaction and coordination. A One Health approach to governance at this level, which has gained traction throughout the COVID-19 pandemic, was shared as a promising mechanism to support a balancing of roles and agendas in this space. However, this must involve agreement around equity, priorities, and clear goal setting to support effective action.

Recent and ongoing global health crises, including the COVID-19 pandemic and outbreaks of mpox and Ebola, have highlighted the urgent threat presented by emerging zoonoses [ 1 ]. Many policy responses to emerging zoonoses have focused on controlling human-to-human transmission, but leading organisations and experts, including the United Nations Environment Programme, the International Livestock Research Institute, and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, have called for a greater focus on the drivers of zoonotic disease emergence in both animal and human populations [ 1 , 2 , 3 , 4 , 5 , 6 ]. This approach, sometimes called deep prevention, would need to target upstream drivers to reduce the risk of outbreaks occuring [ 7 ]. However, consensus on strategies to foster prevention of emerging zoonoses among transnational organisations are in the early stages of development.

Within this approach, the wildlife trade is a key arena for intervention [ 8 ]. In this analysis, we define wildlife trade broadly, as including domestic or international and legal or illegal trade of specimens originally sourced from the wild, traded alive or dead, involving whole individuals or body parts across all life stages for various uses including food, fashion, medicine, zoos and pets [ 9 ]. The wildlife trade may contribute to the risk posed by emerging zoonoses through numerous pathways, including human exposure to zoonotic pathogens through wildlife consumption and handling; transmission of pathogens from rural areas to densely populated urban ones in order to sell wildlife specimens; transmission of pathogens within and between countries and regions when animals are sold and transported through trade networks; increased contact between animal species (including both wild and domesticated animals) across various stages of wildlife trade; and changes in wildlife population dynamics with knock-on effects for reservoir populations, habitats, and disease ecology [ 8 , 10 , 11 ].

Despite the importance of interventions targeting the wildlife trade for reducing risk from emerging zoonoses, effective and coordinated action in this area is challenging. First, responsibility for the governance of wildlife trade spans multiple sectors, including food security and safety, economic development and biodiversity conservation, and the issue has additional relevance for sectors including trade, as well as human and animal health [ 12 ]. The goals of these sectors may sometimes be in conflict, meaning that approaches to governing the wildlife trade must be carefully negotiated to agree on reasonable trade-offs. Second, the global dimension of the wildlife trade – with some wildlife and wildlife products being traded across national borders – makes interaction between countries essential to effective intervention. Finally, international collaboration can help strengthen domestic capacity for wildlife trade governance through policy development, implementation support and sharing expertise and best practice. The inter-sectoral and international nature of this issue makes interaction crucial, and transnational organisations may play an important role in supporting this.

In addressing cross-sectoral problems, such as the wildlife trade, a One Health perspective, which recognises the health of humans, animals, and the environment as closely linked and inter-dependent [ 13 ], can strengthen policy and governance approaches. Building on this perspective, a call has been made by experts and organisations including the World Health Organisation, the Food and Agriculture Organisation, the World Organisation for Animal Health, and the United Nations Environment Programme for a more coordinated and collaborative approach at the international level to prevent the spillover of zoonotic pathogens in human populations [ 14 , 15 ].

While this call for more coordinated and collaborative action has been issued by intergovernmental organisations that set international guidelines and standards and support member states through training and other measures, the nature of the existing network of organisations working on the governance of the wildlife trade at the transnational level has not been characterised. The extent of interaction between organisations and sectors to reduce the risk of zoonotic disease emergence from the wildlife trade is unknown. Although the value of interaction is acknowledged, many parts of the global health landscape are fragmented for reasons including the large and growing number of actors; lack of centralised leadership; and competing interests [ 16 ]. This problem may be even more severe in cross-sectoral contexts, such as wildlife trade governance and the prevention of emerging zoonoses [ 12 ].

Aims and scope

The aim of this study is to characterise the structure and function of the global network of transnational organisations that interact as part of the governance of the wildlife trade, and to assess these network characteristics in terms of how they might support or undermine effective progress on the prevention of emerging zoonoses.

Approach and design

This analysis consisted of a mixed methods social network analysis (MMSNA) of the network of transnational organisations contributing to the governance of wildlife trade (including legal and illegal trade conducted for all uses). Networks are simultaneously structure (i.e., a combination of connections between elements), process (i.e., a way in which these elements and connections evolve in response to each other), and function (i.e., the outcomes these elements and connections are oriented towards the extent to which they achieve them), and can therefore be usefully characterised using both quantitative and qualitative approaches [ 17 ]. Qualitative data can provide information on how the network is evolving, as well as rich contextual information about the relationships and internal workings of structures identified through quantitative approaches.

We employed an MMSNA to understand the structure, processes, and function of this network. MMSNA takes a number of forms, with applications differing in how they integrate quantitative and qualitative data [ 17 , 18 , 19 ]. In particular, the purpose of integrating these two types of datasets may vary, informing how and when data is integrated. In this study, we implemented an explanatory sequential design [ 20 ], using quantitative network analysis to inform the analysis of qualitative data (Fig. 1 ). Through this approach, we aimed to develop a deeper understanding of the functions and processes underlying our structural findings [ 21 ].

Methodology flow chart illustrating participant recruitment, data collection and data analysis

Participant recruitment and data collection started in May 2021 and ended in September 2022.

The study was approved by the Human Participants Review Sub-Committee of York University’s Ethics Review Board (certificate #e2020-310) and conforms to the standards of the Canadian Tri-Council Research Ethics guidelines. Interview participants consented to participate either verbally or in writing before the start of the interview. Survey participants indicated their consent using an online form before starting the survey.

Participant recruitment

This study took a relational approach to drawing the boundaries of the network [ 22 ], starting from a seed sample and asking participants to identify additional actors who they thought belonged to the network. All participants were identified via professional networks and desktop searches for relevant organisations, as well as snowball sampling, and recruited by email.

All participants were invited to complete the survey, but only a subset of participants were invited to participate in interviews.

Interview participants were key informants based in transnational organisations (i.e., organisations that operate in a way that transcends national borders [ 23 ]), primarily in intergovernmental organisations (i.e., UN agencies or other bodies established through agreements between national governments) and treaty secretariats, involved in issues relating to the governance of the wildlife trade, human health, animal health, international trade and food security, and the prevention of emerging zoonoses. We selected interview participants based on the relevance of their expertise and roles to our research aims, and we stopped interview recruitment and data collection when we reached data saturation [ 24 ]. Interview participants served as a seed sample: each participant was asked to share organisation names and contact details of professionals that they interacted with around the governance of the wildlife trade and the prevention of emerging zoonoses at other organisations. Potential participants were emailed up to three times with an invitation to participate. Where organisation names were provided without contact details, additional contacts based at named organisations were identified through desktop searches and invited to participate via email or social media.

Organisations were considered to be the actors in this network (i.e., the unit of analysis), and recruitment strategies for the survey targeted one representative from each organisation, rather than attempting to recruit all potential individuals working in this area. Depending on the size of the organisation and the breadth of its scope, we recruited participants in a leadership position for the organisation as a whole, or for the portion of its work that was most closely linked to the governance of the wildlife trade. We stopped survey recruitment and data collection when we reached data saturation (i.e., survey responses ceased to identify additional organisations), and at least one contact at each identified organisation had been invited to participate three times. In introducing the survey to participants, we emphasised that we were interested in the interactions of the organisation as a whole, and invited potential participants to forward the survey to a colleague who might be best placed to speak from the organisational perspective, if they did not feel able to do so, and advised participants that they could gather input from colleagues to complete the survey if they felt this was appropriate.

In a small number of cases, more than one participant was recruited from a single organisation, where participants referred a colleague within their own organisation as an additional participant to provide a fuller picture of their organisation’s activities. In this case, data from those participants were combined as representing the range of interactions and activities undertaken by an organisation.

Data collection

The data set consisted of survey and semi-structured interview data.

Survey data was collected through the online survey platform SmartSurvey [ 25 ]. Participants were asked to identify their own organisation as well as other organisations they interacted with around wildlife trade governance and the prevention of emerging zoonoses. Specifically, they responded to the questions: “Do you interact with anyone at any of the following organisations around the governance of wildlife trade and/or the prevention of emerging zoonoses?” (participants could select as many organisations as they wanted from a list) and “Do you interact with anyone at any organisations other than those listed above around the governance of wildlife trade and/or the prevention of emerging zoonoses?”.

Interview data was collected through semi-structured key informant interviews by MW and EGC. Each interview lasted approximately an hour and was conducted through the online video platform Zoom [ 26 ]. Interview questions focused on topics including international wildlife trade management; response to zoonotic disease outbreaks and pandemics; and coordination in efforts to govern the wildlife trade and reduce related public health risks. Interviews were audio recorded and transcribed verbatim.

Where participants completed both the survey and the interview, the survey was completed after the interview, with participants being sent a link to the survey to complete it in their own time.

Based on a review of organisational websites, organisations were classified by two characteristics to facilitate analysis. First, organisations were classified by the sector to which they belonged (Table 1 ), including organisations working in each of the three sectors typically associated with the concept of One Health (animal health, human health, environmental health) [ 13 , 27 ], organisations working in other sectors, and ‘One Health’ organisations. Organisations were also classified by organisation type adapted from an existing typology of organisational actors in global health: consultancy; government department; inter-governmental organisation; network; non-governmental organisation; professional association; regional economic initiative; research institution; trade association; treaty secretariat; voluntary partnership secretariat [ 28 ].

First, we analysed quantitative data by calculating network statistics developed in the field of social network analysis [ 29 ]. We considered the network to be a binary, undirected network [ 29 ]. We analysed survey data in R [ 30 ]. We used the igraph package [ 31 ] to calculate nodal properties and identify sub-groups, the statnet [ 32 ] and ergm [ 33 ] packages to assess homophily and the ggraph package [ 34 ] to visualise the network.

We evaluated the centrality of nodes – in our case, organisations – within the network, which was treated as undirected. Centrality assesses the extent to which organisations are involved in network relationships, and can be characterised in different ways [ 35 ]. We calculated three measures of centrality within this network: [ 35 ]

Degree centrality : How many organisations a given organisation is directly connected to, signifying how active and well-connected that organisation is within the network;

Betweenness centrality : How often an organisation appears within the shortest path between organisations, indicating the extent to which that organisation can act as a gatekeeper or broker in terms of the flow of information or resources within the network; and.

Closeness centrality : How close (i.e., how many relationship steps away) an organisation is to other organisations, indicating independence or efficiency as an actor within the network, as the organisation does not depend on others to connect or communicate with partners [ 29 ].

As well as assessing the centrality of particular nodes within the network, we also identified cliques : sub-groups of three or more organisations within the network, all members of which are connected to one another [ 29 , 35 ]. Members of one or more cliques tend to be core members within the network. We therefore identified the largest clique, as well as the organisations belonging to the greatest number of cliques.

We also assessed homophily within the network: the tendency of nodes to be connected to nodes with similar traits. We assessed whether organisations from the same sector (animal health, human health, environment, or One Health) were more likely to be connected to organisations from the same sector, in order to assess the extent to which the network is characterised by cross-sectoral interaction. To assess network homophily, we used exponential random graph modelling (ERGM), a modelling approach which takes into account key characteristics of network data, particularly that the observations within a network are highly inter-dependent, and that network sampling is purposive rather than probability-based [ 29 ]. ERGM has been explained in greater detail elsewhere [ 36 ]. Briefly, ERGM relies on the generation of networks with the same structural properties as the observed network (known as ‘degree randomisation’), creating a distribution of possible networks. The observed network is then compared to this distribution, allowing a range of hypotheses about the network’s properties to be tested. In this case, we used ERGM to assess whether the observed homophily by sector was higher than what was likely to be explained by network structure alone, after adjusting for organisation type.

Second, we analysed qualitative interview data. Based on our MMSNA approach, using qualitative data to investigate ideas suggested by quantitative findings, we developed research questions to explore in the qualitative data. Thematic analysis was conducted using the approach described by Braun and Clarke where the focus is guided by the researcher’s analytic interests [ 37 ], with the four research questions informed by the quantitative findings serving as an a priori coding framework. We subsequently identified sub-themes through close reading and inductive coding of the transcripts. Thematic analysis was conducted by three researchers working together (CCA, AD and RM), using an iterative process to develop meaning through discussion and repeated review of the data and codes [ 38 ]. To support this process, we used the collaborative qualitative data analysis software Dedoose [ 39 ].

Sample characteristics

Semi-structured interviews were conducted with an initial sample of 17 key informants from 14 organisations identified through professional networks. By pursuing the network connections of these interview participants, 69 organisations were identified as part of the network of transnational organisations focused on the governance of wildlife trade and the prevention of emerging zoonoses. Of the 133 potential participants invited (covering all 69 organisations), 37 participants from 33 organisations completed the network survey (with respondents from two larger organisations suggesting multiple participants to represent the breadth of their work). At the organisation level, this resulted in a response rate of 48% (33/69 organisations in the network). Response rates were slightly higher for organisations in the animal health and One Health sectors (45% in human health, 60% in animal health, 43% in environmental health, 63% in One Health, 53% in other). For organisation types, response rates from networks were highest, while response rates from non-governmental organisations were lowest (58% in intergovernmental organisations, 39% in non-governmental organisation, 60% in agreement secretariats, 71% in networks, 45% in research institutions, and 55% in other, including organisations such as consultancies, government departments and professional organisations). Characteristics of organisations identified within the network are described in Table 2 . Characteristics of interview participants are described in Table 3 .

Network description and organisation centrality

The identified network was composed of 280 connections (characterised here as interactions identified by survey participants) between 69 organisations (Fig. 2 by organisation sector and Fig. 3 by organisation type). The network’s diameter (the length of the longest path between two organisations) is 5. The average degree (average number of connections per organisation) is 4.06.

Network mapping of organisations involved in the prevention of emerging zoonoses and the management of the wildlife trade. Node colour indicates sector. The figure highlights the lack of centrality of organisations in the human health sector, despite their relatively high number

Network mapping of organisations involved in the prevention of emerging zoonoses and the management of the wildlife trade. Node colour and shape indicates organisation type. Organisation types have been grouped together for easier visualisation (treaty secretariat, voluntary agreement secretariat = agreement secretariat; consultancy, government department, professional association, regional economic initiative, trade association = other.) Inter-governmental organisations have relatively high centrality

Organisations varied in terms of degree, betweenness and closeness centralities (organisations with the highest degree, betweenness and closeness centralities in Table 4 ; all centrality information in Supplementary File 1 ). While a substantial number of organisations in the network focused on human health (33%, see Table 2 ), organisations with high centrality were predominantly focused on One Health, animal health, and environmental health. This suggests that, while many organisations focused on human health are included in the network, organisations in other sectors, particularly animal health, environmental health and One Health, are more connected with multiple organisations across it, having higher degree, betweenness and closeness centrality. Inter-governmental organisations showed high centrality across all three measures, suggesting that these organisations are key actors within the network. Two of the seven network organisations are situated within the top ten organisations for some measures of centrality. This may suggest that some of these network associations have been more successful than others in connecting previously unconnected organisations.

Clique identification

The largest clique in the network was composed of ten organisations. This was dominated by inter-governmental organisations ( n = 6), but also included two non-governmental organisations, one research institution, and one network organisation. A range of sectors were represented. (One Health n = 1, environmental health n = 4, animal health n = 2, development n = 1, food and agriculture n = 1, human health n = 1), although environmental health dominated. The organisations in this clique also appeared as frequent members of other, smaller cliques. This suggests that a core group of organisations, particularly inter-governmental organisations, work on these topics together, which may allow them to share information, resolve issues and reach consensus on how to move forward within a sub-group, which could be shared across the larger network to facilitate collective approaches and action.

ERGM to assess homophily by sector

Table 5 shows the results of the ERGM testing homophily by sector. Model results do not support the hypothesis that organisations from the same sector are more likely to be connected.

Research questions informed by quantitative findings

The network mapping and statistics informed the development of four additional research questions about the functioning of the network. These were explored through thematic analysis of semi-structured interview data:

Are there sectoral differences in how active, independent, and powerful organisations are within this network?

How have network organisations impacted the network’s ability to interact effectively.

How does the core group of organisations identified (i.e., the largest clique identified through the quantitative social network analysis) impact interaction across the network?

Are there differences in how organisations interact within versus outside their own sector?

While participants identified a will to collaborate across sectors, they also highlighted some competition for resources and tension in terms of different goals:

“I mean, so in other words, whilst we [sectoral organisations] have separate goals, […] if we’re centralised we’ll always have a different perspective, there will always be conflict. And whoever is stronger and more dominant, will essentially win. […] Until you get around the table in a balanced way, it will always shift to the strongest sector.” (Key informant, animal health)

Overall, the power and resources held by the human health sector were repeatedly emphasised, despite its relatively low centrality within this network. This was discussed at the international level with reference to governance structures such as the Tripartite (now the Quadripartite) [ 13 ] and the ability to put in place legal instruments such as the International Health Regulations (IHR): [ 40 ]

“I mean you know the Tripartite is something, it’s good, but the honest truth in my opinion about the Tripartite is it’s been doing two-thirds health.” (Key informant, other) “Yes, IHR is interesting. And of course, it comes back to the power issue. So they’ve been able to introduce a regulatory framework that is very helpful if you’re dealing with human infection, [but] it’s almost impossible to get a diagnosis very often in wildlife, because the regulatory framework, for example, moving samples around the world are so complicated, so difficult.” (Key informant, animal health)

The animal health and, in particular, the environmental health sectors, were seen as less well-resourced, and tended to be less dominant in decision-making processes:

“The environment sector is under-resourced and can’t take on yet another challenge.” (Key informant, One Health).

However, the specific topics on which this network focused may explain the centrality of the animal and environmental health sectors. Organisations focused on human health did not always see the relevance of the wildlife trade, the prevention of emerging zoonoses or related issues such as environmental protection to their organisational remit:

“We talk to the health ministry well and they find it interesting what we do, but somehow they still think that we’re keeping animals healthy and it has nothing to do with them.” (Key informant, One Health)

In some cases, this made the human health sector less likely to engage on these topics. While it was perceived that some actors in the human and animal health sectors acknowledged the importance of the environment to health, this did not necessarily translate to a sense of responsibility for environmental issues:

“And sometimes it’s easier to convince people in the health sector that the [environment] plays an important role for health, and changes within the environment do influence the health of people and animals. So that’s sometimes easier to get across to get the health sector convinced, but then they don’t feel responsible, you know? So if it’s major conservation we don’t do that. Or even climate mitigation or adaptation, that’s not for us.” (Key informant, One Health)

However, many key informants emphasised the role that paradigms such as One Health and planetary health, as well as health crises such as the COVID-19 pandemic, had played in highlighting the relevance of these topics for human health, and building more interest and enthusiasm from the human health sector:

“And increasingly that’s very much going into strengthening the interaction with partner agencies is moving into the prevention part of it because, for example, the current pandemic has shown us the limitation of how prepared can we be and how we can respond to these type of events showing that it was clear this time that once these new pathogens are out of the box so to speak it’s simply too late to control them. So being able to do more the prevention is something we want to investigate in the future.” (Key informant, human health)

Key informants also emphasised that in some cases the human health sector needed the animal health sector to work in the area of emerging zoonoses, as animal health experts typically had more extensive knowledge of pathogens that were common in animals before being transmitted more frequently to humans. These instances could foster equitable relationships and interaction:

“And often veterinary services are seen as a lesser partner sometimes by public health [but avian] influenza was a good example, from my experience, because the public health sector really needed us. They needed the viruses, they needed the information from the animal health sector to inform public health strategy. And very importantly, to develop a vaccine if they needed to. So it was a very equitable relationship.” (Key informant, animal health)

Conversely, the opportunities presented by the human health sector’s platform and resources were also emphasised by some participants: the involvement of this sector had the potential to raise the profile of issues relating to the wildlife trade and the governance of emerging zoonoses.

While there were sectoral differences in power and investment across the network, many participants saw the One Health approach, which was seen to have increasingly gained traction during the COVID-19 pandemic, as a mechanism to give the animal and environmental health sectors more of a seat at the table. However, many cautioned that this approach could become tokenistic unless it was based on specific goals shared by the sectors, and supported by a clear plan for implementation.

Key informants’ perspectives aligned with the quantitative finding that network organisations (i.e., organisations whose explicit goal is to bring actors together) were not playing a key role in creating new connections between organisations in the network. Participants reported that they had had some interactions with many of the organisations in the network for many years, independent of the network organisations, some of which had only emerged fairly recently.

However, network organisations were seen to support interaction in other ways. Network organisations contributed to centring previously marginalised interests, such as the health of wildlife populations, and working to convene parties and facilitate new work and interactions on these topics:

“So this is where we see our role is really as a convenor in some ways. It’s to unite around shared goals with a shared vision. That was something from our experience that was quite important, to develop a joint vision that we can all get behind. And of course that’s a challenge in itself because if it gets too vague then anybody will just kind of align behind it and that was not really unifying either. And so it’s not easy.” (Key informant, One Health)

The services they provided to their members included highlighting potential areas of synergy, where different organisations could work together on projects that would benefit them both. For example, where multiple organisations were seeking funds for projects with overlap in terms of geography or disease focus, network organisations connected organisations to develop more competitive and resource-efficient funding applications. Network organisations also provided connections to relevant experts and supported partnership development to access and use resources more effectively. They also worked with organisations and governments at the national level, connecting countries to support learning and information sharing. For example, where a national government expressed interest in improving their capacity to conduct risk assessments in wildlife markets, a network organisation organised an information session on the topic, inviting representatives from other countries to attend and share information, resources and lessons from their own efforts in the area.

How does the core group of organisations impact interaction across the network?

The core group of organisations seemed central to the functioning of the network. They played a convening function, bringing together different organisations both within and between countries through mechanisms such as working groups or expert panels, sharing information, and providing a platform to build buy-in from national-level actors. They also advocated across sectors, making the case for disease in wildlife populations as a shared threat, and highlighting cases where interventions were a ‘win-win’ for multiple sectors. This enabled them to involve new stakeholders and build buy-in. Finally, this core group of organisations had a certain institutional memory of collaborative work, and could draw on existing networks and modes of working that had been put in place during previous outbreaks of zoonotic disease.

However, this core group had certain limitations in its activities. Transnational organisations typically interfaced with their sectoral equivalent within a national context (e.g., a transnational animal health organisation would mainly interact with a national-level animal health organisation). This meant that, while inter-sectoral coordination was happening transnationally, in-country work depended on existing coordination between sectors in a given country context. This inter-sectoral interaction was not always present at the national level:

"[…] over the past year, we’ve been surveying our member countries on to what extent they are involved in regulating wildlife trade. And it varies from country to country. […] And we can have an effect working through veterinary services but only if the veterinary services are a working partnership with other actors at national level, whether that be wildlife, health, or foreign authorities or environment agencies, or whatever." (Key informant, animal health)

There was also a perception that this core group – dominated by inter-governmental organisations – faced bureaucratic barriers to action and interaction, and may struggle to respond to rapidly changing issues such as the prevention of emerging zoonoses:

“The bureaucratic barrier can hinder efficient collaboration among global government offices due to entrenched working habits, which may take years to change.” (Key informant, animal health)

Bureaucratic barriers identified by participants included a misalignment between sectoral mandates and responsibilities at national and transnational levels, particularly for an area such as the wildlife trade which may be governed by different sectors in different contexts:

“Or they may not have the mandate – in one country it might be the public health institute that has the mandate for food safety regulations in a food market, in another place it might be an agricultural department, right. So, because wildlife suffers the fate of being of interest to everybody, nobody wants to own it. And because there’s no minister of coordination, right, there is no department of integration, there’s nobody who develops the strategic advantages by working in cross-sectors.” (Key informant, animal health)

Participants also reported bureaucratic barriers to coordinated transnational action on animal health which were not present in the human health sector. For example, one participant reported challenges in identifying disease outbreaks in animal, and particularly wildlife, populations, due to regulations preventing sending samples across national borders for timely testing. While the International Health Regulations supported these procedures for human populations, the same measures were not in place for wildlife.

In addition, while the core group was cross-sectoral, there was a perception that the learnings or commitments that emerged from their interactions did not necessarily diffuse into their respective organisations. Finally, there was concern that this core group kept certain highly relevant voices out of mainstream decision-making, particularly actors from the environmental health sector: animal and, particularly, human health agencies tended to be more powerful and better funded, and the relevance of the environmental health sector to decision-making was not always recognised, either by the environmental health sector itself or agencies from other sectors.

Key informants reported that the wildlife trade was outside the traditional remit of the animal health sector, which typically focuses on providing veterinary care for domesticated animals, as well as outside the human health sector, despite the risk presented to human health by emerging zoonoses and the relevance of the wildlife trade to human health topics such as food safety. However, many stated that this had changed somewhat because of the COVID-19 pandemic. As a result, the governance of the wildlife trade necessitated inter-sectoral interaction, which may explain the lack of homophily by sector within this network: some level of inter-sectoral interaction was required to address a topic that does not fall clearly into the remit of any one sector.

At the same time, participants highlighted distinctions between intra- and intersectoral interaction. Interaction across sectors typically focused on ad hoc connections, such as establishing working groups or developing training for on-the-ground staff that needed a more holistic skill set. This type of work depended on mutual willingness and the recognition of shared aims, as well as the availability of resources to support sustained engagement. Another main type of interaction involved learning from other sectors’ expertise, which could also avoid a duplication of efforts to build up expertise within each sector:

“So we don’t come into it trying to be health specialists, if you know what I mean. We’re coming in with a wildlife [perspective] and seeing how we can add that understanding to people and organisations that have an animal health or public health focus.” (Key informant, environment)

Participants acknowledged that there were substantial barriers to inter-sectoral interaction, however. Typically, sectors had different goals, terminology, and metrics for success, as well as different governance structures. Some key informants saw the conflicting goals as a strength and an opportunity for learning:

“So it’s definitely a strength to work with people that do not have the same perspective and the same interest. The key thing is to define this common interest.” (Key informant, other)

While all key informants acknowledged the importance of cross-sectoral interaction around the governance of wildlife trade and the prevention of emerging zoonoses, some acknowledged that working within a sector could sometimes be a more expedient way of making progress.

Statement of principal findings

The network of transnational organisations focusing on the governance of wildlife trade is composed of many types of organisations from sectors including human, animal, and environmental health. Our findings highlight the intensely inter-sectoral nature of this area, and a desire among its members to see greater interaction. This interaction was supported by the establishment of network organisations and the efforts of a core, multi-sectoral group of organisations that was well-connected and influential within the transnational network as well as with national-level actors.

However, inter-sectoral interaction was often challenging due to conflicting aims and perspectives. The network was also impacted by power dynamics: the human health sector, while historically less involved in wildlife trade governance, was seen as better resourced, more powerful, and influential. This was seen as both a boon for the network and its interests, as the involvement of the human health sector brought resources and a larger platform, and a risk to equity between actors. A One Health approach was seen as a potential way to build decision-making and governance processes on a more equitable footing, but key informants emphasised the importance of clarity around goals and implementation for such an approach.

Strengths and limitations

This network was conceptualised as a binary undirected graph based on participant responses about organisational interactions. This meant that connections identified by one organisation were taken to be mutual, and the intensity and more specific nature of relationships was not considered. This decision was made after piloting the survey instrument: we found that participants were unwilling to characterise the nature and frequency of professional interactions between their organisations’ many partners. The time taken to complete this more detailed survey instrument may have caused participants to give up before completing the survey, a comment explicitly made in some survey responses. We therefore simplified the survey instrument and analysis approach to look at interactions more broadly defined, characterised simply as existent or non-existent. As a result, our quantitative analysis could not incorporate more nuanced aspects of organisational relationships. Our characterisation of ‘interactions’ therefore conceals complexity: interactions may have consisted of activities including resource-sharing, information sharing, or providing and securing agreement, and even included interactions that may have been antagonistic, as well as collaborative. However, by complementing the survey data with qualitative data, we were able to explore aspects of network process and function that could not be captured through the survey, which helped to capture nuance which might have been missed if focusing only on the quantitative analysis. For example, our quantitative results suggested that organisations were not more likely to be connected to organisations within their own sector compared to other sectors. However, our qualitative analysis highlighted that while cross-sectoral connections existed, the nature and intensity of these connections did vary by sector.

Our study also faced two of the key challenges which are typical of organisational network analysis. First, while the analysis was carried out at the level of organisations, our participants were individuals working within those organisations, targeting a key informant in each organisation in line with typical practice in organisational network analysis [ 41 , 42 , 43 ]. It is therefore possible that their perspectives did not include all of their organisations’ interactions [ 44 ]. We tried to mitigate this by emphasising that we were interested in the interactions of the organisation as a whole; by inviting potential participants to forward the survey to a colleague who might be best placed to speak from the organisational perspective, if they did not feel able to do so; and by advising participants that they could gather input from colleagues to complete the survey if they felt this was appropriate.

Second, given that we received responses from less than half of the organisations identified in the network, our data set is likely to be characterised by data missingness, which is a key challenge in survey-based research, and network analysis in particular [ 29 , 44 ]. At the data collection phase, we attempted to mitigate this by following up with participants up to three times over the space of several months; seeking personalised introductions; and inviting additional participants from the same organisations where no response was received. At the analysis phase, we included inter-organisational ties where they were confirmed by a single organisation, in order to minimise the impact of missing data on our findings. The centrality measures discussed here, and other node-level characteristics, are sensitive to missing data, and higher amounts of missing data lead to lower correlation between the ‘real’ and measured properties of nodes, though this effect may vary across networks with different properties [ 45 ]. In this analysis, measured centrality of groups of organisations with lower response rates, namely organisations in the human and environmental health sectors and non-governmental organisations, may therefore be less accurate than measured centrality for other groups. However, it is not known whether this would have led to an over- or under-estimation of organisation centrality: one of the under-represented groups, the environmental sector, was found to be highly central, while the remaining groups were not. Our mixed methods approach also adds depth to our understanding, allowing us to interrogate the findings from the quantitative analysis, identifying points where both data sets are aligned and divergent.

Finally, network analysis provides a snapshot of a network at a given point in time. While the mixed methods approach allowed us to understand how the network had been evolving, particularly during the COVID-19 pandemic, the network has most likely continued to evolve since data collection ended.

Implications for policy and practice

In this analysis, we found that the COVID-19 pandemic, and other zoonotic epidemics and pandemics, played a key role in fostering greater multi-sectoral interaction across this network. As our qualitative findings suggest that, while organisational interactions happened across sectors, inter-sectoral work is challenging, that working within one sector may be more expedient, and that bureaucratic barriers to agile action exist at this level of governance, the importance of building cross-sectoral governance structures in ‘peace time’ – when international health crises are not driving urgent action – seems clear.

The importance of inter-sectoral work and the value of a One Health approach to the governance of wildlife trade and the prevention of emerging zoonoses seems broadly recognised by our key informants. However, participants stressed that this must be more than a rhetorical position, and must be supported by clear goals and strategies for implementation. Our findings highlight that, while the organisations in this network are broadly in agreement with the importance of One Health, they may have strongly conflicting views around what the term means and what the approach looks like in practice.

While this analysis focused on the transnational level of governance, the interplay between these actors and national and subnational governments is key to making recommendations based on these findings. With the exception of the treaty secretariats that featured in the network, most of the organisations do not have a power to compel, and instead act by convening different parties, developing standards and providing information and expertise. Within these boundaries, the organisations within this network can build on their successful practice of sharing information, identifying and connecting experts, developing guidance, and convening countries for mutual learning. However, it would be important to build the inter-sectoral, One Health approach to these issues into their interactions with national governments, as this could also diffuse the approach to the national and sub-national level.

Comparison to existing literature

To our knowledge, this is the first global network analysis focused on the governance of wildlife trade within the context of prevention of emerging zoonoses by transnational organisations, though a country-level network analysis of UK-based organisations combatting the illegal wildlife trade was published pre-pandemic [ 46 ]. While some international actors were identified as belonging to this country-level network, these were much more focused on conservation, animal welfare and crime prevention (e.g., Interpol, International Institute for Environment and Development, International Fund for Animal Welfare, World Wildlife Fund). Organisations focused on agriculture and food security, which were identified as part of the network in the current study, were not identified. This may reflect the UK-focused study’s emphasis specifically on illegal wildlife trade, which may not be considered for its contribution to food security in the same way, as well as its lack of emphasis on zoonotic disease, which centres interactions between wildlife, livestock, and people – including in food-relevant sites such as farms and markets – as potential drivers of disease transmission.

Health-focused network analyses of global actors are also relatively rare, although an existing analysis of the global health space more broadly found similar types of actors involved, including non-governmental organisations, inter-governmental organisations, professional associations and national governments with an international remit [ 23 ]. In contrast to this analysis, we did not identify any industry bodies as being a part of this network. While key informants mentioned the relevance of certain industry sectors, such as agriculture and pharmaceutical development, to this area, these actors were not named as being directly part of the network. The wildlife trade itself did not seem to have a representative industry body with ties to this network. At other levels of governance, our findings were aligned with other network analysis studies which have highlighted the wide range of sectors involved in the control of zoonotic disease, and highlight the role of disease outbreaks in fostering greater inter-sectoral interaction and more openness to a One Health approach [ 47 ].

Our findings align with previous studies on barriers to effective One Health efforts. Existing literature also highlights some of the issues identified in our study, including professional divisions between human, animal and environmental health practitioners and policymakers; differences in terminology; and a lack of coordination and collaboration [ 48 , 49 , 50 ]. However, in contrast to studies focused on One Health topics more broadly, which often find the environmental health sector to be under-represented in One Health efforts [ 49 , 51 ], this sector was fairly central to our network, perhaps because of the network’s focus on wildlife, and particularly the wildlife trade, as opposed to domesticated animals. While several recent disease outbreaks have been linked to changing human-wildlife interactions [ 10 , 11 ], wildlife and wildlife trade predominantly fall under the remit of environmental protection, hence environmental health, with much global governance of wildlife being focused on conservation [ 12 , 52 ]. Indeed, the wildlife trade, both legal and illegal, has been identified as one of the major drivers of biodiversity loss, which has corresponded with policy responses at various governance levels [ 52 , 53 , 54 ]. Our analytical approach also yielded new insights around the relative centrality of the human health sector in this particular One Health topic: while many of the organisations in the network belonged to the human health sector, the position of these organisations within the network seemed to suggest that it may be less central to the network.

Finally, our study seems to confirm findings from similar studies on network governance in global health, which have emphasised the importance of lead organisations within networks to generate policy momentum. When studying organisational networks, an essential question is how the collaboration between actors in these systems is governed. Provan and Kenis proposed three ways in which organisational networks could be governed: through ‘shared governance’, with wide-spread distribution of power and decision-making functions; through ‘lead organisation governance’, with one member organisation leading governance; or through ‘network administrative organisation governance’, with a dedicated network administrative organisation to govern the network [ 55 ]. Our network does not seem to fit neatly into any of the three categories, but most closely resembles a shared governance approach in which the network is governed by all organisations interacting with each other, resulting in a somewhat decentralised network, though a core group of organisations was central to network governance. Inferring from studies of network governance focused on other global health topics [ 56 ], a dedicated network administrative organisation for wildlife trade might be an important step towards improving network governance in this policy space, which may also improve its effectiveness.

Future research

While our quantitative analysis was cross-sectional, qualitative findings highlighted the impact the COVID-19 pandemic was continuing to have on this network. Additional research could continue to explore how this network is changing over time; for example, by taking a longitudinal network analysis approach [ 44 ]. This approach is useful when evaluating the effectiveness of interventions to strengthen interaction and connection across the network, and could be used to evaluate the impact of mechanisms to strengthen interaction, such as the ‘network organisations’ that we identified.

In addition, research is needed to better understand how cross-sectoral interactions, such as those occurring in this network, can strengthen relational coordination, defined as “a mutually reinforcing process of communicating and relating for the purpose of task integration” [ 57 ]. A survey tool can be used to evaluate the extent of frequent, timely, accurate, and problem-solving communications and to gauge the level of shared goals, shared knowledge, and mutual respect required for effective collaborative initiatives [ 58 ]. Structures that support greater relational coordination can help network actors to think about working towards shared goals and understanding how their own contributions and those of others contribute to shared outcomes [ 57 ]. Stronger relational coordination may therefore contribute to more effective action in complex networks such as the one analysed in this study, where competing goals can impede progress. Assessing and improving relational coordination can help reframe the discussion around risk mitigation and the need to revise existing policy, considering the expertise, competencies, and experiences of cross-sectoral networks.

Finally, further research could assess to what extent the current level of interaction in this network supports or undermines these organisations’ capacity to prevent the emergence of zoonoses in human and animal populations. While key informants seemed to broadly share the assumption, common in network-focused literature [ 59 ], that more interaction across this network would support more effective action, this assumption could be further explored empirically. Multi-sectoral collaboration is often seen as positive, especially for complex problems where bringing together knowledge and resources can support new ways of thinking and the implementation of more effective solutions, particularly where action from multiple actors may be needed to solve a problem [ 59 ]. However, these interactions come at a cost in terms of time and resources [ 60 ], and the extent to which potential benefits outweigh costs should be assessed.

The network of transnational organisations focused on the governance of wildlife trade is highly multi-sectoral, but barriers still exist to inter-sectoral interaction.

This study highlights some important ways in which the network can be strengthened, including by continuing to build and invest efforts into multi-sectoral governance and action in a sustained way, moving from a more reactive stance to one focused on prevention and preparedness. This will require the continued support of the human health sector, which has sometimes seen issues, such as wildlife health, as beyond its remit. Transnational organisations may also explore options for connecting with country-level partners in a more cross-sectoral way, helping to mainstream a One Health approach to policy and governance. Finally, a One Health approach to governance at this level, which has gained traction throughout the COVID-19 pandemic, may be a mechanism to support more equitable balancing of roles and agendas in this space. However, this must involve agreement around priorities and clear goal setting to support effective action.

Data availability

Because the global governance community interviewed as part of this study is relatively small and closely knit, full interview transcripts cannot be shared beyond the project team as this may compromise participants’ anonymity. The anonymised quantitative data set is available from the corresponding author on reasonable request. Analysis code for the quantitative analysis is available on GitHub.

Abbreviations

Mixed methods social network analysis

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Acknowledgements

We would like to thank all study participants for generously contributing their time and insights to this study.

This study was undertaken as part of a project funded by the Canadian Institutes of Health Research, Grant Reference Number VR5-172686. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CCA and TLP acknowledge internal research support from York University. MW and CCA acknowledge internal research support from the Dahdaleh Institute for Global Health Research. EGC was supported by the Cedar Tree Foundation and the Society for Conservation Biology through the David H Smith Conservation Research Fellowship Program. KML acknowledges funding from the Canadian Institutes of Health Research through a Health System Impact Fellowship. AMV acknowledges support from York University through a York Research Chair in Population Health Ethics & Law.

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Conception and design: CCA, TLP, EGC, MW. Acquisition of ethics certificate: CCA, TLP, EGC, MW. Acquisition of data: MW, EGC, CCA, KML. Analysis and interpretation of data: CCA, AD, RM, TLP. Drafting of the manuscript: CCA. Critical revision of the manuscript for important intellectual content: AD, EGC, MW, CA, KC, AR, AMV, PT, TLP. Obtaining funding: TLP, MW.

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Clifford Astbury, C., Demeshko, A., Gallo-Cajiao, E. et al. Governance of the wildlife trade and the prevention of emerging zoonoses: a mixed methods network analysis of transnational organisations, silos, and power dynamics. Global Health 20 , 49 (2024). https://doi.org/10.1186/s12992-024-01055-7

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v.12(11); 2020 Nov

The COVID-19 Infodemic: A Quantitative Analysis Through Facebook

Naseem ahmed.

1 Pathology, Dow University of Health Sciences (DUHS), Karachi, PAK

Tooba Shahbaz

2 Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK

Asma Shamim

Kiran shafiq khan.

3 Internal Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK

S.M. Hussain

Social media is a crucial part of our daily life. Facebook, being the biggest social media platform, plays a significant role in the spread of information influencing the global response to the COVID-19 pandemic. Health care agencies like the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) use social media as a platform to impart information regarding COVID-19; simultaneously, there is a spread of misinformation on social media, masking the credible sources of information. Our research aims to assess the utility of Facebook in providing misinformation and testing its “fact-check policy.”

An online search was conducted on Facebook by a newly created account to eliminate bias. The Facebook search bar was used to investigate multiple keywords. Data were tabulated in Microsoft Excel (Microsoft Corporation, Redmond, WA). Descriptive statistical analysis of Facebook accounts and posts was done using the Statistical Package for the Social Sciences (SPSS) version 26 (IBM Corp., Armonk, NY) while statistical importance was set a priority at a p-value of 0.05.

Our study consisted of 454 Facebook posts. Most (22.5%) were posted by verified accounts and 23.9% by informal individual/group accounts. The tone for most (40.4%) COVID-19 information was serious while the most common (43.9%) topic was medical/public health. In total, 22.3% included misinformation, 19.6% were unverifiable, and 27.5% included correct information verifiable by the WHO or CDC.

Conclusions

Misinformation/unverifiable information related to the COVID-19 crisis is spreading at a distressing rate on social media. We quantified the misinformation and tested Facebook’s “fact-check policy.” We advise strict initiatives to control this infodemic and advise future researches to evaluate the accuracy of content being circulated on other social media platforms.

Introduction

Social media has become a crucial component of our everyday life in today’s globalizing society. It has a penetration of 40.9% of the entire world population, and it is estimated that, globally, 2.95 billion individuals are using social media as of 2019 [ 1 ]. Facebook has become the biggest social media platform universally since around 2.6 billion monthly active users were reported in the first few months of 2020, [ 2 ]. Therefore, social media, specifically Facebook, plays a significant and crucial role in the spread of information across the world, influencing the global response to this pandemic.

Coronavirus disease 2019 (COVID-19) causes a severe respiratory illness similar to severe acute respiratory syndrome [ 3 ]. Recent evidence shows that the COVID-19 virus spread to human beings through transmission from wild animals that were illegally sold in the Huanan seafood market [ 3 ]. Phylogenetic analysis shows that the COVID-19 virus is a new member of the Coronaviridae family but is separate from severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) [ 3 ]. The typical symptoms of COVID-19 are cough, dyspnea, sore throat, fatigue, and, most commonly, fever that occurs soon after exposure to a carrier. It may lead to pneumonia and severe disease as well, especially for the elderly [ 3 ].

In this modern era, where technology is a click away, healthcare agencies like the World Health Organization (WHO) [ 4 ] and the US Centre of Disease Control and Prevention (CDC) [ 5 ] use social media as a platform to impart up-to-date information regarding COVID-19. Simultaneously, numerous rumors, misinformation, myths, and hoaxes have appeared on social media too, consequently drowning our credible sources of information, which have collectively received only some hundred thousand engagements [ 6 ]. Whether well-intentioned or malevolent in nature, this plethora of misinformation leads to a fear of an otherwise low-mortality infection; inappropriate prescribing and overdosage of harmful drugs, decreasing healthy behaviors, and promoting unfitting practices, hence resulting in suboptimal control of the COVID-19 crisis across the globe [ 7 ].

To scrutinize the reliability of its content, Facebook uses the International Fact-Checking Network’s Code of Principles to choose fact-checking partners from all over the world [ 8 ]. When any data is considered incorrect, Facebook alerts users who have recently interacted with the said post and then reduces that post’s visibility to other Facebook users [ 9 ]. Despite these efforts, it was flooded with misinformation, forcing Facebook to announce updates and stricter policies in March 2020 [ 8 ].

Keeping this infodemic challenge in mind, our research aims to assess the utility of Facebook in providing misinformation, unverified information, and correct information regarding the coronavirus, concurrently testing the updated fact-check policy of Facebook.

Materials and methods

We conducted web-based research using the Facebook platform from July 14, 2020, to July 16, 2020. A newly created account was used to eliminate bias in the search results. We used the Facebook search bar using multiple keywords, as mentioned in Figure Figure1. 1 . Our search was restricted to posts in the English language only and to those having a minimum of 10 likes. We eliminated posts that had less than 10 likes Data of all the posts in the results of the searched hashtag were collected that included any information relating to coronavirus and were tabulated in Microsoft Excel (Microsoft Corporation, Redmond, WA). Our research did not require the approval of the institutional review board (IRB) since all the data that we collected was from public accounts.

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Each post was categorized as correct information, misinformation, or unverifiable information. They were cross-checked with the information provided by the updated guidelines of the World Health Organization (WHO) and the Center for Disease Control and Prevention (CDC) [ 4 - 5 ]. Those corresponding to the information were characterized as correct information. Posts that included any content that could be easily be rebutted using any of the aforementioned sites were considered misinformation while we categorized unverifiable information as posts that could not be proven either correct or incorrect by the same sources.

For each individual post, a couple of predetermined variables were collected by the authors regarding their content and account characteristics. Accounts were categorized into the following categories: nongovernmental organization (NGO), government, news outlet, healthcare, and informal individual/group. Account verification was also charted. A verified account is defined as one of public interest that is deemed to be authentic by Facebook. The contents of each post were classified according to its tone and topic. The categorization of topics was health, financial, and sociopolitical. The tone of content was categorized into the following: serious, humorous, and opinion. Posts categorized as serious were those containing information related to COVID-19, posts labeled as opinion were those that portrayed the account's own point of view and humorous posts were those containing memes or jokes.

Statistical analysis

Descriptive statics were taken to analyze Facebook accounts and post characteristics. Chi-square statistics were utilized to determine p-values for the relation between accounts/posts characteristics and the presence of misinformation, unverifiable information, and correct information. Bar graphs were created using Microsoft Office Word. Statistical importance was set priority at a p-value of 0.05. All analysis was executed using the Statistical Package for the Social Sciences (SPSS) version 26 (IBM Corp., Armonk, NY).

Accounts and post characteristics

Our study consists of a total of 454 Facebook posts. Most of them were posted by verified accounts (291, 44.5%), followed by informal individual/group (156, 23.9%). Of all the accounts, the least was posted from NGOs (30, 4.6%) and government accounts (12, 1.8%) (Table (Table1). 1 ). In Table Table2, 2 , we present the characteristics of the individual posts. The bulk of information regarding COVID-19 included serious content (264, 40.4%) while only 84 (12.8%) posts contain humorous content. The most common topic was medical/public health (287, 43.9%), followed by socio-political (96, 14.7%) and financial (68, 10.4%).

NGOs: nongovernmental organizations

Characteristics	N (%)
Verified Facebook account	291(44.5%)
Informal individual/groups	156(23.9%)
Healthcare/public health	130(19.9%)
News outlet/journalist	114(17.4%)
NGOs	30(4.6%)
Government	12(1.8%)

Characteristic	N (%)
Tone
Serious	264 (40.4%)
Humorous/non-serious	84 (12.8%)
Opinion	99 (15.1%)
Topic
Public Health/Medical	287 (43.9%)
Financial	68 (10.4%)
Socio-political	96 (14.7%)

Misinformation, correct, and unverifiable information

In total, 146 posts (22.3%) included misinformation, 128 (19.6%) included unverifiable information and 180 (27.5%) include correct information verified by WHO or CDC (Figure (Figure2). 2 ). Informal individual/group accounts had more misinformation when analyzed Facebook post by post category (65, 40.1%, p: <0.001) (Table (Table3). 3 ). In addition, the same category posted the most unverified information (44, 27.2%). Government, NGOs, news outlets/journalists, and healthcare/public health accounts all had a low rate of misinformation respectively, shown in Table Table3. 3 . Moreover, Facebook posts posted by verified Facebook accounts included more unverified information when compared to those posted by unverified accounts (unverified account: 26.4%, verified account: 29.2%, p: <0.001) vice versa for posts with false information where misinformation is posted more from unverified accounts than verified (unverified: 40.5%, verified: 27.5%). News outlet/journalist accounts (16.5%) contain the lowest rate of misinformation as compared to other accounts. Furthermore, public health accounts maintain the second-highest record of posting misinformation (51, 38.3%). The number of likes per post and the number of shares per post show no association, with any significant difference in terms of unverifiable and misinformation rates (p>0.05) (Table (Table3 3 ).

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Post/Account characteristic	Misinformation, N (%)	p-value	Correct information, N (%)	p-value	Unverifiable information, N (%)	p-value
Healthcare/public health		<0.001		0.076		0.45
Yes	51(38.3%)		53(39.8%)		29(21.8%)
No	82(61.7%)		80(60.2%)		114(78.2%)
NGO		0.034		0.001		0.025
Yes	8(28.6%)		11(39.3%)		9(32.1%)
No	20(71.4%)		17(60.7%)		19(67.9%)
News outlet/journalist		0.040		0.004		<0.001
Yes	19(16.5%)		52(45.2%)		44(38.3%)
No	96(83.5%)		63(54.8%)		69(60%)
Government		0.001		0.07		0.43
Yes	3(80.8%)		10(62.5%)		3(18.8%)
No	13(81.2%)		6(37.5%)		13(81.2%)
Informal individual/groups		<0.001		0.98		0.77
Yes	65(40.1%)		53(32.7%)		44(27.2%)
No	97(59.9%)		109(67.3%)		115(71%)
Verified account		<0.001		0.07		0.001
Yes	80 (27.5%)		126(43.3%)		85 (29.2%)
No	66 (40.5%)		54(33.1%)		43 (26.4%)
No. of likes
<1000	140(35.6%)	0.067	141(35.9%)	0.01	112(28.5%)	0.065
>1000	6(9.8%)		38(62.3%)		17(27.9%)
No. of shares
<500	135(34.5%)	0.077	143(36.6%)	<0.001	113(28.9%)	0.090
>500	11(17.5%)		36(57.1%)		16(25.4%)

Accounts with a higher number of shares and likes contain correct information (62.3%, 57.1%, respectively) demonstrating that more likes and shares are associated with correct information (p<0.01). Conclusively, the frequency of misinformation varied among hashtags, presenting that the hashtag "#vaccinedevelopment" had the highest rate of misinformation (Figure (Figure3), 3 ), the hashtag "#vaccinedevelopment" had the highest rate of unverifiable information, while "mask cam" and "#coronavaccine" had the lowest (Figure (Figure4 4 ).

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The SARs-CoV2 virus emerged from the Hubei province in China in late 2019, but soon, the entire earth got inundated by this viral disease [ 10 ], resulting in numerous hospitalizations in the early months of 2020. Focusing on the devastating effects of the virus, WHO declared it a “global health emergency” [ 11 - 12 ]. Soon enough, social media got plagued with colossal amounts of information linked to the virus [ 13 ], as the public not only input content on social media but also used it as a means to seek health information and news from over the world. Posts ranged from individual preventive measures (e.g. the effectiveness of masks in containing the contagion [ 14 ] to treatment availability (e.g. reports on hydroxychloroquine [ 15 ]). This process enabled an explosion of unchecked information and the spread of misinformation. To our knowledge, our research is the first study that uses Facebook as a platform for analyzing the misinformation ratio.

In our study, the total 454 posts on Facebook were posted by accounts owned by healthcare organizations 130 (19.9%), news outlets 114 (17.4%), NGOs 30 (4.6%), governments 12 (1.8%) as well as other informal individuals/groups accounts 156 (23.9%). They all played a massive part in posting information concerning different aspects of COVID-19 with informal individuals/groups (156; 23.9%) posting the highest content. Similar research pertaining to the coronavirus infodemic on Twitter tabulated their results as healthcare/public health 73 (10.8%), news outlets 111 (16.5%), business/NGO/government 37 (5.5%), medical/public health 468 (69.5%), and informal individual/group 448 (66.6%) posting the second-highest content. As serious as this infection is, most of the posts were serious in nature to 264 (40.4%). Coronavirus has not only affected our health but also the socio-political aspects of human lives; similarly, most of the topics of the posts were related to health 287 (43.9%) while some pertained to socio-political topics 96 (14.7%). Nonetheless, posts related to finance (68; 10.4%) also took part in disseminating information, as this virus has a negative impact on job security, resulting in a global economic recession and increased expenditure in terms of safety gadgets like masks, gloves, sanitizers, personal protective (PPE), etc. Conclusively, almost all types of content, whether serious 264 (40.4%), humorous 84 (12.8%), expressing opinion 99 (15.1%) - heath, sociopolitical, or financial-related topics - emerged on Facebook and are constantly being uploaded by accounts of different backgrounds. Similar COVID-19 infodemic data collected on Twitter [ 16 ] tabulated different topics and tones of Tweets as follows: serious Tweets (614; 91.2%), humorous or non-serious Tweets (41; 6.1%), opinion (144; 23.0%), public health Tweets (468; 69.5%), Tweets regarding finance (38; 5.6%), and sociopolitical tweets (242; 40.0%). This study also concluded medical health to be the most common topic discussed on Twitter.

Similar to our study, people use Facebook to seek help and educate themselves, however, the “tsunami of information” holds an alarmingly high risk of misinformation and unverifiable information. Misinformation is defined as a “claim of fact that is currently false due to lack of scientific evidence" [ 17 ]. Distribution of fallacious content across different social media has become a common practice and there is no exception for the COVID-19 crisis [ 18 ]. Finally, on February 15, 2020, the general director of WHO stated that “We’re not just fighting an epidemic; we’re fighting an infodemic” [ 19 ]. Social media was the culprit of the dissemination of false news most notably during the 2016 U.S. presidential campaign as well [ 20 ]. Another prominent example is the rise of numerous anti-vaccine campaigns when social media became the medium of propagation of inaccurate and harmful information [ 21 ]. This has happened over the years since social media and Facebook give the freedom to post any content whether it is verified by scientific evidence or not and the easy access to sharing Facebook posts disseminates such content like wildfire. Our results depict that misinformation and unverifiable information collectively is more than correct information on Facebook, hence verifying the former. Our results are also in line with Gunther Eysenbach’s information wedding cake model where Gunther et al. depict social media as the largest segment of the cake, representing the vast amount of nearly unfiltered and uncontrolled information contributed or amplified by the public [ 22 ].

Similar circumstances occurred during the time of the Zika virus epidemic, misguidance, and false news spread. Neeraja et al. in their study stated that on Facebook, accurate information about the Zika virus and its disease was less popular than erroneous videos and posts [ 23 ].

The fight against the infodemic is a real challenge, as it spreads very rapidly on social media. Tustin et al. and Xu et al. also reported widespread misinformation about side effects, as well as mistrust in government or pharmaceutical companies in discussions on vaccination [ 24 ]. False information over vaccines seems like an incessant trend. A COVID-19 vaccine is still under development, however, as our results interpret, fallacious content relating to it already exists on social media since the hashtag “vaccine development” was associated with a higher rate of misinformation (45.2%) and unverifiable information (28.7%). This suggests a continuous distrust of the public with vaccines, therefore, any future post regarding vaccines should be strictly under observation, whether pertaining to COVID-19 or not. Considering the aforementioned history, we predict that once the anti-COVID-19 vaccine is invented, it might have immense mistrust among the public, with hoaxes and myths spreading across social media, resulting in a large population of people not accepting the vaccine. If strong and clear-cut statements are not made exposing and condemning misinformation, it may have a devastating effect on the public [ 6 ]. The COVID-19 crisis already causes increased anxiety and has an unprecedented impact on mental health [ 25 ]. False information just adds more fright and unhealthier behaviors. For example, in March 2020, hundreds of Iranian citizens died after ingesting alcohol in a bid to treat COVID-19 as a result of misinformation circulating on social media [ 26 ].

Research conducted on the spread of misinformation about Ebola during its epidemic concluded that the quantity of incorrect information was low; similarly, our data collected in the month of July portray that the presence of false and unverifiable information is less than correct information, yet they collectively exist in a significant amount versus correct information. These results are also parallel to those collected on Twitter during the month of February 2020 where misinformation and unverifiable information had a strong relationship with user account verification [ 27 ], as our sources of misinformation were also mostly from unverified accounts. Henceforth, Facebook should keep a stricter check on public unverified accounts, as they are a major source of distribution of unverifiable and misinformation.

VP Integrity of Facebook, Guy Rosen, in his updated letter on April 16, 2020, claims that Facebook removes or limits the spread of false information related to COVID-19 that could cause impending harm to the public. They either tag the content as false or remove it completely [ 28 ]. They have previously removed false information about rumors of the polio vaccine from Pakistan where it risked the ill-treatment of medical professionals [ 8 ]. Fortunately, in our study, the number of likes with correct information (38; 62.3%) and shares with correct information (36; 57.1%) were significantly more than likes with incorrect (6; 9.8%) or unverifiable information (17; 27.9%) and shares with incorrect (11; 17.5%) and unverifiable information (16; 25.4%), implying that the popularity of correct posts was more among users, as they received more engagement from the users. This could be the result of Facebook users not sharing content that was flagged as false or could entirely be because of the reduced visibility of erroneous content to the public. This is in contrast to Xinning et al., according to whom, people retweet jokes and sports events more regarding the Zika virus epidemic especially when those jokes and events include unverified information [ 23 ]. Another study’s findings pertaining to the information of vaccines on YouTube by Gabrielle et al. state that videos with positive and correct information were viewed and shared less and had fewer likes than those with a negative tone and material [ 19 ]. This may suggest a better content verifying system of Facebook as compared to not only Twitter but also YouTube, which are a couple of the most used social media platforms. Hence, it is safe to say that Facebook’s fact-check policy is succeeding in limiting the spread of false information as compares to other social media.

In a population with a low literacy rate, health-related myths but the mostly increased availability of free time to the general public all over the world as a result of the COVID-19 lockdown may have contributed to the infodemic even more. Health literacy is defined as the individuals’ capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions and to address or solve a health-related problem. Reports show that a rumor has a three times greater chance to be shared on social media than a verified story. Lack of health literary and health myths, as well as free time, will only amplify the spread of such posts. There’s no ambiguity that it is tough and takes decades to tackle the problem of illiteracy in populations. Also, there is complete uncertainty of the uplifting of lockdowns in various parts of the world. However, it is easier to keep a check on the quality and quantity of information flow on social media, which will, no doubt, ease the panic and control harmful and unhealthy practices.

Despite our promising results showing trends in the inflow of information among Facebook users, our study has a few limitations. First, our study was limited to the English language, which may have generalized our results for non-English speakers. Second, the use of specific hashtags as search terms might have resulted in authors missing those posts that did contain COVID-19 information but didn’t utilize the hashtags while posting their content. However, we selected the most common hashtags, which were a total of 17 in number, greater than any keywords used in previous similar researches carried out on Facebook, and included all the posts resulting from those hashtags for three days [ 29 ]. Lastly, our search timeframe was restricted to a few days and, therefore, might not have captured the changing topics that might have advanced with the pandemic. This invites additional research to fill the vital information gap. Nevertheless, our research not only provides timely data but also proves the validity of Facebook’s promising fact-check policy.

As recommended by Gunther Eysenbach’s fourth pillar of infodemic management [ 22 ], we also suggest that continuous monitoring and analysis of data and information flow patterns on social media should be undertaken so that outbreaks of misinformation, rumors, and falsehoods could be detected immediately and countered with facts or other interventions like flagging or removing the content from the social media platform, consequently decreasing the dissemination of negative information and panic among the public. Leticia et al. stated that limiting the spread of incorrect information by algorithmic and social corrections is also effective [ 20 ]. Content should not only be checked after it has been posted, but it should be verified before it is made visible to the public so that we could cut the cycle of the spread of misinformation before it starts. This should at least be applied over trending topics and posts getting larger engagements, likes, and shares. The quality of information, i.e. correct or incorrect, as well as the quantity and distribution in social media both, should be kept under check. Moreover, social media requires generating metrics not only of information supply but also of information demands, i.e. search queries and hashtags, for better control of any future infodemic.

Misinformation and unverifiable information pertaining to the worldwide COVID-19 crisis is proliferating at a disturbing rate on social media. We quantified the misinformation spread and tested Facebook’s “fact-check policy.” Our research also provides an initiative for future researches to evaluate the accuracy of content being circulated on various other social media platforms like Instagram, WhatsApp, YouTube, etc., which are widely used by the general public, and study the impact of the propagation of rumors on behavior and precautionary habits that people need to adopt in times of public crises. Interventions from relevant authorities are crucial in order to harness the positive power of social media to distribute accurate and error-free information, as it affects herd behavior. Facebook’s fact-check policy is doing wonders; yet, there still exists a margin for improvement.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

Human Ethics

Consent was obtained by all participants in this study

Animal Ethics

Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

What is omnichannel marketing?

Picture this: you’re browsing online for some new work clothes, and you add something to your virtual cart but ultimately decide not to buy it. Then later, you see an ad on social media for the abandoned garment. Some scratch their heads at this, but it’s actually an example of omnichannel marketing.

The prefix “omni” means “all,” and “channel” is a reference to the many ways customers might interact with a company—in physical stores, by surfing the web, on social media, and in emails, apps, SMS, and other digital spaces. And this omnichannel approach can be a powerful way to meet your customers where they are, providing them good service in line with their preferences and needs. (Note that, in this article, we use the terms “customers,” “consumers,” and “shoppers” interchangeably in referring to omnichannel marketing in both B2B and B2C contexts.)

More and more, customers move across all channels—in person, online, and beyond—to get what they want. But not every customer is looking for the same thing, and omnichannel marketing acknowledges that. Some people want more services for certain transactions; others prefer low-touch, 24/7 interactions. Effective omnichannel marketing , then, happens when companies provide a set of seamlessly integrated channels, catering to customer preferences, and steer them to the most efficient solutions.

So why is omnichannel marketing important? Research on the omnichannel experience shows more than half of B2C customers engage with three to five channels each time they make a purchase or resolve a request. And the average customer looking to make a single reservation for accommodations (like a hotel room) online switched nearly six times between websites and mobile channels. If these customers encounter inconsistent information or can’t get what they need, they may lose interest in a brand’s products or services.

And this can translate into business outcomes. Omnichannel customers shop 1.7 times more than shoppers who use a single channel. They also spend more.

Sometimes the term omnichannel is used in the context of customer service or customer experience . And it’s also used as a descriptor of other elements that go into supporting an organization’s omnichannel efforts—for instance, omnichannel supply chains , which is shorthand for an approach in which companies ensure that their supply chains are optimally set up to support omnichannel marketing efforts.

What are examples of omnichannel?

Omnichannel approaches are commonly used in retail (both B2B and B2C ), but you’ll also find it in healthcare and other spaces. Medtech companies , for instance, use a variety of channels including digital marketing, inside sales, portal and e-commerce, and hybrid sales-rep interactions to engage with healthcare professionals.

Several omnichannel examples can illustrate various approaches:

Best Buy typically focuses on commerce (both in store and online), but boosted its in-store experience by creating offerings for customers to explore smart home-technology solutions, pairing them with free in-home advisory services. And its mobile app lets customers “scan to shop” from catalogs and curbside, or buy online and pick up merchandise in the store itself, smoothing the end-to-end journey for customers with the 24/7 tech support from its Geek Squad. Best Buy’s Totaltech support offer was compelling to customers—it launched with 200,000 memberships in 2018, which climbed to two million within a year.
Beauty retailer Sephora emphasizes omnichannel personalization, relying on rich in-app messaging, personalized push notifications, and easy ways for customers to book in-person consultations. Its in-store technology is a powerful complement that allows employees to access customer favorites and suggest products they might try next. Its loyalty program also plays an important role. The efforts are already driving value for Sephora: data showed that customers visiting the retail website within 24 hours of visiting a store were three times more likely to make a purchase, and orders were 13 percent higher than for other customers.
Nike takes an ecosystems view of omnichannel, extending the brand experience and offering customers an ever-growing platform of content, offers, and community interactions. Its SNKRS and Run Club apps, for example, facilitate in-person meetups, running groups, and events. It also has an app for delivering individual workouts and fitness programs, creating experiences that go far beyond shoe and apparel lines to meet customers in their day-to-day routines.

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How has omnichannel been affected by the COVID-19 pandemic?

Omnichannel rose during the COVID-19 pandemic as more consumers turned to e-commerce. Due to the increased demand for contactless shopping during the height of the pandemic, US grocery stores saw 20 to 30 percent of their business shift to online . Before the pandemic, e-commerce accounted for just 3 to 4 percent of total sales for grocers.

The shifts made during the pandemic are likely to persist . In the pandemic, people gravitated to curbside pickup, “buy online, pay in store” models, and self-checkout at higher rates than in the past. And recent research indicates these behaviors are “sticky”—indeed, about 70 percent of people who first tried self-checkout in the pandemic say they’ll use it again.

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What do customers want out of the omnichannel experience.

Customers want a compelling and personalized omnichannel user experience with robust digital capabilities, both online and offline. About 60 to 70 percent of consumers research and shop both in stores and online . More concretely, over one-third of Americans made omnichannel features—think buying online and picking up in store or curbside—part of their regular shopping routines since the COVID-19 pandemic emerged. Nearly two-thirds of those individuals plan to continue doing so. And younger buyers, like Gen Zers, embrace omnichannel enthusiastically; these customers don’t think in terms of traditional channel boundaries, and they expect brands and retailers to provide a seamless experience, no matter where they are.

In a sense, all customers are omnichannel customers now, McKinsey partner Tiffany Burns explains in an episode of the McKinsey on Consumer and Retail podcast :

“Many retailers still think, ‘There are omnichannel interactions and store interactions, and I’m optimizing those two things separately. I have two different teams working on and thinking about those experiences.’ But as a consumer, when I go on the retailer’s website or app, I expect to see availability, a connection to what’s in the store, and a way to order things that I can pick up in store. I also expect to be able to stand in the aisle in the store and research a product. Today, consumers are figuring out workarounds to do all those things: they’re switching over from the app to Google, looking up the product, and searching for reviews.”

Organizations that make shopping a seamless omnichannel experience , or provide an app that helps customers find their way or see what’s in stock in the store, are already creating experiences that are a win for omnichannel customers .

Is omnichannel the same thing as phygital? And what is phygital, anyway?

Omnichannel is a business strategy, while “phygital” (a portmanteau that combines the word “physical” and “digital”) refers to the integration of the physical and digital worlds.

The term suggests a completely connected world that is both physical and digital at the same time . While fewer consumers are visiting brick-and-mortar stores and choosing to use e-commerce instead, more than 80 percent of retail sales still occur in a physical location . By 2030, the shopping experience will be highly personalized , and some activities may even take place in the metaverse . According to a recent survey, 80 percent of US adults want personalization from retailers with multiple, personalized touchpoints , which can include a mobile app, digital displays, interactive screens, tech-enabled associates, and point of sale.

What about omnichannel vs multichannel?

When it comes to omnichannel vs multichannel, the key difference is the focus at the center of all efforts. Omnichannel is a customer-centric approach in which all channels are integrated so the customer has a unified and consistent experience whether they are at a physical store, using an app, or on a website. Multichannel, in contrast, tends to revolve around products instead of customers. It aims to inform as many people as possible about the product or brand, and the channels are not linked, so the customer experience is often different for each channel.

An interview with an insurance executive, Eric Gewirtzman of BOLT , makes that distinction relatable: “Insurance customers are already moving between various channels,” Gewirtzman says. “But there’s a big difference between being multichannel and being omnichannel. Just because carriers have, say, an exclusive agent channel, an independent agent channel, and a website, doesn’t mean they’re omnichannel. Too often, consumers will get a different experience and different results depending on which channel they use. This has to change. If there is no awareness between the channels, sales are lost.”

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What is omnichannel personalization?

Omnichannel personalization refers to the way organizations might tailor the customer experience for individuals across physical and digital channels. This includes multiple touchpoints that cater to the customer’s preferences pre-visit, during the visit, and post-visit. Customers receive products, offers, and communications that are unique to them as individuals.

Efforts to personalize the omnichannel market can have a big payoff. Indeed, getting omnichannel personalization right could help companies increase revenue by 5 to 15 percent across the full customer base.

While companies recognize the power of omnichannel personalization, they may face roadblocks in implementing these efforts for a variety of reasons:

Omnichannel personalization requires a lot of investment in technology (both software and hardware). Personalizing physical spaces often starts from scratch because it requires enabling digital touchpoints such as screens, kiosks, or tablets for store associates, which may not exist.
It is difficult to deliver a seamless customer experience and train employees. The front line needs training to understand and reinforce the customer journey.
Traditionally, companies operate their digital and physical channels independently. Omnichannel personalization requires companies to rethink their organizational structure across both the digital and physical parts of the business .

These barriers, however, can be overcome. Five steps can help companies achieve omnichannel personalization :

Define the omnichannel personalization strategy and learning agenda. It’s crucial to develop a clear view on key moments of influence in the customer journey , and then identify what outcomes are desired at each step of that journey. Finally, an organization needs to prioritize use cases to test, looking at their ability to deliver business benefits and value to customers.
Address five digital touchpoints to activate personalized experiences in physical environments. Companies need to connect digital and physical footprints to drive omnichannel personalization, especially at touchpoints where these worlds converge. Five are particularly important: mobile apps, digital displays, interactive screens, tech-enabled associates, and point of sale.
Use an omnichannel “ decisioning engine ” to deliver experiences and measure performance. This can help organizations identify, quickly and accurately, the next best action to take with each customer.
Implement agile operating practices. Personalized marketing goes beyond mere technology; it requires new ways of working, and agile marketing teams can help in this regard.
Activate omnichannel personalization in the field. To bring all these elements together, a company’s sales force must be fully aligned and well trained. In-person teams could make your customer’s day, so frontline personnel need to support personalization efforts, understand their value, and use digital tools to deliver the complete experience.

What is omnichannel strategy?

An omnichannel strategy for marketing is a way of ensuring that your efforts drive tangible business value. Rather than rushing blindly into the space, or haphazardly approaching it, organizations should step back and think about underlying business value drivers. Excelling in omnichannel depends on a laser focus on value creation, looking at both strategic and customer priorities to craft the omnichannel strategy that will be most effective for their unique circumstances.

The most successful companies set their omnichannel strategy by leading with their strategic ambition and aspirations for customer experience. There are three primary omnichannel strategies :

Commerce. This prioritizes cross-channel shopping experience both in store and online.
Personalization. This strategy focuses on tailored, targeted, and relevant cross-channel engagement at scale.
Ecosystem. Here, the strategy aims to provide rich cross-channel platforms integrated with consumer needs and lifestyles.

Learn more about our Growth, Marketing & Sales practice.

What’s involved in omnichannel operations?

Organizations can build leading omnichannel operations , spanning a variety of areas. By strengthening the foundation of your omnichannel operations and focusing on strategy, structure, and processes, you could gain a performance edge.

Topics to explore include mastering omnichannel supply chains , creating a customer-centric supply chain strategy , designing the omnichannel distribution network of the future , reimagining the role of physical stores , and more.

What should I know about B2B omnichannel?

Omnichannel has become a permanent part of B2B sales , with e-commerce, face-to-face, and remote videoconference sales all a necessary part of buyers’ experience. According to a 2021 McKinsey survey of US-based B2B decision makers, 94 percent of respondents view today’s B2B omnichannel reality as being as effective or more effective than before COVID-19. The findings also revealed that B2B customers regularly use ten or more channels to interact with suppliers, up from five in 2016.

B2B omnichannel efforts can be a path to grow an organization’s market share, but loyalty is up for grabs, with customers more willing than ever to switch suppliers for a better omnichannel experience. B2B decision makers use more channels than ever before to interact with suppliers, and being attuned to those channels will be important.

There are five must-dos for B2B companies seeking to retain customer loyalty and succeed in omnichannel:

offer a performance guarantee (nearly 80 percent of B2B customers say it’s crucial)
show product availability online
enable purchases over any channel
provide customer service in real time
ensure the customer experience is consistent as buyers toggle between channels

While B2C omnichannel efforts might be the first to spring to mind, omnichannel experience is crucial to giving all customers a better and more seamless journey.

For more in-depth exploration of these topics, see McKinsey’s insights on marketing and sales —and check out omnichannel-related job opportunities if you’re interested in working at McKinsey.

Articles referenced include:

“ The new B2B growth equation ,” February 23, 2022, Arun Arora , Liz Harrison , Max Magni, Candace Lun Plotkin , and Jennifer Stanley
“ Omnichannel: The path to value ,” April 30, 2021, Holly Briedis, Brian Gregg , Kevin Heidenreich, and Wei Wei Liu
“ Omnichannel shopping in 2030 ,” April 9, 2021, Praveen Adhi , Eric Hazan , Sajal Kohli , and Kelsey Robinson
“ Redefine the omnichannel approach: Focus on what truly matters ,” June 22, 2020, Jorge Amar , Raelyn Jacobson , Becca Kleinstein, and Allison Shi
“ The end of shopping’s boundaries: Omnichannel personalization ,” February 10, 2020, Gal Gitter, Meg Raymond, Kelsey Robinson , and Jamie Wilkie

Want to know more about omnichannel marketing?

Omnichannel: The path to value

The new B2B growth equation

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