Primary health care systems in sub-Saharan Africa (SSA) need context-specific evidence to address current challenges. Increased family physician (FP) research activity could help fill this gap.
To describe the research activity, facilitators and barriers amongst AfriWon Renaissance members.
An online programme was designed to improve research activity amongst members of AfriWon Renaissance, an organisation of early-career and trainee FPs in SSA. This article provides a baseline description of their research activity.
All AfriWon Renaissance members were invited to participate in an online survey. A content-validated study tool assessed research activity, including participation in research meetings, engagement in research mentorship, number of projects and published articles. Facilitators and barriers were assessed via Likert scales and two open-ended questions. The researchers conducted descriptive statistics using Epi Info 7, a content analysis of open-ended responses and triangulation.
Amongst the 77 respondents, 49 (63.6%) were still in training. Over two-thirds (71.4%) had participated in a research discussion in the past month. Whilst more than half (63.5%) reported having a manuscript under development, only 26 (33.8%) reported a recent publication. Nearly all (94.8%) intend to continue research in their FP careers. The most common facilitators were the institutional requirement to conduct research and having supportive peers and mentors. The most predominant barriers were time constraints and a lack of training on analysis.
There is a cohort of committed young FP researchers who would benefit from efforts to address identified barriers and support for their ongoing research activity, in order to increase primary care research outputs in SSA.
There is a growing global recognition of the connection between strong health research capacity, translation of evidence into practice and the achievement of positive health care outcomes. Although health research has been linked to systems-strengthening and universal health care achievement, research capacity gaps remain pronounced in low-income and middle-income countries (LMICs), where health system strengthening is most needed.
Numerous health research capacity strengthening (HRCS) efforts have attempted to address this gap in research production in SSA, both in general and specific to primary care.
In 2019, a pilot research training and mentorship initiative called AfriWon Research Collaborative Program was designed to increase the level of research activity amongst early-career FPs in SSA.
An online cross-sectional survey was conducted consisting of both closed and open-ended questions.
AfriWon Renaissance (AfriWon) is a professional organisation of early-career and future FPs under the aegis of a professional group of FPs in Africa. All survey participants consisted of AfriWon members who were either (1) trainees in a family medicine residency programme in SSA or (2) early-career FPs living in SSA who were within five years of completing their training. These survey responses were used as the baseline for a longitudinal evaluation of the AfriWon Research Collaborative Program, which will be further discussed in a forthcoming publication. As all modules and communication were in the English language, limited readers, nonreaders and nonspeakers of the English language were excluded.
The researchers used a voluntary response sampling strategy which allowed participants to take part in the survey through a link that was distributed via the social media platforms of AfriWon and its email list. The survey was open for one month and participation was incentivised through a raffle draw for a primary care e-book.
The researchers generated survey items to describe research activity by adapting a previously published point system for family medicine trainee scholarly activity.
How many active research projects are you involved in right now?
In this past month, have you participated in any research meetings, forums, chats or discussion groups?
How many research protocols have you had approved by an institutional review board (IRB?)
Closed-ended questions on research mentorship and an item exploring plans for future research activity was also included. To describe facilitators and barriers to research, the researchers used common thematic areas from their review of the literature
Research is undertaken by other health workers where I work.
I have access to electronic databases.
My employer or supervisor provides me with an adequate amount of time to conduct research.
The survey also included two open-ended questions asking for participants to state their three most important facilitators and barriers. See Online Appendix 1 for the entire survey tool.
Following the methods described by Zamanzadeh et al., the researchers calculated the content validity index of the study tool, retaining items with an item content validity index (I-CVI) score of greater than 0.79 and modifying those with I-CVI score of lesser than 0.79 for clarity.
Descriptive analysis of demographic data was done using means, standard deviations, frequencies and proportions of data. The analysis was done using Microsoft® Excel® and Epi Info 7™.
An inductive content analysis approach was used to analyse open-ended survey data on facilitators and barriers to research activity.
The study compared and matched responses from the quantitative analysis of facilitators and barriers with the categories derived from the qualitative content analysis. The data were displayed in a table for interpretation in order to identify the facilitators and barriers that were most common across both analyses, as well as the novel ones identified through open-ended responses alone. The researchers used member checking
Ethical clearance was obtained from the Boston University Institutional Review Board (Protocol H-38521) and the Federal Medical Centre, Keffi Health Research Ethics Committee in Nigeria (reference number FMC/KF/HREC/299/19). All respondents gave written informed consent prior to participation.
Amongst all participants (
Demographic characteristics of participants.
Demographic characteristic | All ( |
Trainees ( |
Graduates ( |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean | s.d. | % | Mean | s.d. | % | Mean | s.d. | % | ||||
38.78 | 9.0 | - | - | 36.42 | 5.0 | - | - | 43.32 | 12.6 | - | - | |
Male | - | - | 33 | 42.9 | - | - | 21 | 42.9 | - | - | 12 | 44.4 |
Female | - | - | 43 | 55.8 | - | - | 28 | 57.1 | - | - | 15 | 55.6 |
Southern Africa | - | - | 10 | 13.0 | - | - | 7 | 14.3 | - | - | 3 | 11.6 |
East Africa | - | - | 7 | 9.1 | - | - | 3 | 6.1 | - | - | 4 | 15.4 |
Central Africa | - | - | 6 | 7.8 | - | - | 5 | 10.2 | - | - | 1 | 3.9 |
West Africa | - | - | 51 | 66.2 | - | - | 34 | 69.4 | - | - | 17 | 65.4 |
High-income country |
- | - | 1 | 1.3 | - | - | 0 | 0.0 | - | - | 1 | 3.9 |
Single | - | - | 15 | 19.5 | - | - | 10 | 20.4 | - | - | 5 | 19.2 |
Married | - | - | 53 | 68.8 | - | - | 33 | 67.4 | - | - | 20 | 76.9 |
Divorced | - | - | 2 | 2.6 | - | - | 2 | 4.1 | - | - | 0 | 0.0 |
Separated | - | - | 4 | 5.2 | - | - | 3 | 6.1 | - | - | 1 | 3.9 |
Other | - | - | 1 | 1.3 | - | - | 1 | 2.0 | - | - | 0 | 0.0 |
Urban | - | - | 45 | 58.4 | - | - | 30 | 62.5 | - | - | 15 | 55.6 |
Semi-rural | - | - | 22 | 28.6 | - | - | 14 | 29.2 | - | - | 7 | 25.9 |
Rural | - | - | 5 | 6.5 | - | - | 4 | 8.3 | - | - | 2 | 7.4 |
Other | - | - | 3 | 3.9 | - | - | 0 | 0.0 | - | - | 3 | 11.1 |
Note: The table shows the demographic characteristics of participants in the study, with the first column showing all participants and the second and third columns showing the subgroups of our exploratory analysis: trainees and graduates. Standard deviations and percentages were calculated using Microsoft® Excel® and Epi Info 7™.
s.d., standard deviation.
, One participant currently practises in a high-income country but is from the West African sub-region.
,
,
,
Respondents in the study sample exhibited varying levels of research activity, as depicted in
Reported research activity amongst participants.
Research activity item | All respondents |
Trainees |
Graduates |
||||
---|---|---|---|---|---|---|---|
% | % | % | |||||
- | - | - | - | - | - | 0.258 | |
0 | 27 | 35.1 | 19 | 38.8 | 8 | 28.6 | - |
≥ 1 | 50 | 65.0 | 30 | 61.2 | 20 | 71.4 | - |
- | - | - | - | - | - | 0.001 | |
0 | 27 | 36.5 | 3 | 12.0 | 24 | 49.0 | - |
≥ 1 | 47 | 63.5 | 22 | 88.0 | 25 | 51.0 | - |
- | - | - | - | - | - | 0.006 | |
0 | 51 | 66.2 | 38 | 77.6 | 13 | 46.4 | - |
≥ 1 | 26 | 33.8 | 11 | 22.5 | 15 | 53.6 | - |
- | - | - | - | - | - | 0.023 | |
0 | 41 | 54.7 | 30 | 63.8 | 10 | 37.0 | - |
≥ 1 | 34 | 45.3 | 17 | 36.2 | 17 | 63.0 | - |
- | - | - | - | - | - | 0.217 | |
Yes | 55 | 71.4 | 33 | 67.4 | 22 | 78.6 | - |
No | 22 | 28.6 | 16 | 32.7 | 6 | 24.4 | - |
- | - | - | - | - | - | 0.248 | |
Yes | 47 | 61.0 | 28 | 57.1 | 19 | 67.9 | - |
No | 30 | 39.0 | 21 | 42.9 | 9 | 32.1 | - |
- | - | - | - | - | - | 0.016 | |
Yes | 49 | 64.5 | 12 | 46.2 | 37 | 74.0 | - |
No | 27 | 35.5 | 14 | 53.9 | 13 | 26.0 | - |
- | - | - | - | - | - | 0.538 | |
Unlikely | 4 | 5.2 | 3 | 6.1 | 1 | 3.6 | - |
Likely | 73 | 94.8 | 46 | 93.9 | 27 | 96.4 | - |
Note: The table shows the results of a descriptive analysis of collected research activity variables amongst all respondents and an exploratory analysis of these variables within trainee and graduate subgroups.
, Unlikely’ is the combination of ‘extremely unlikely’, ‘unlikely’ and ‘neither likely or unlikely’, whereas ‘likely’ is the combination of ‘extremely likely’ and ‘likely’.
,
,
,
As seen in
Research facilitators and barriers reported by participants.
Facilitator or barrier item |
All respondents |
Trainees |
Graduates |
||||
---|---|---|---|---|---|---|---|
% | % | % | |||||
0.280 | |||||||
Disagree | 35 | 45.5 | 24 | 49.0 | 11 | 39.3 | |
Agree | 42 | 54.6 | 25 | 51.0 | 17 | 60.7 | |
0.137 | |||||||
Disagree | 18 | 23.4 | 9 | 18.4 | 9 | 32.1 | |
Agree | 59 | 76.6 | 40 | 81.6 | 19 | 67.9 | |
0.472 | |||||||
Disagree | 32 | 42.7 | 19 | 39.6 | 13 | 48.2 | |
Agree | 43 | 57.3 | 29 | 60.4 | 14 | 51.9 | |
0.509 | |||||||
Disagree | 53 | 69.7 | 33 | 68.8 | 20 | 71.4 | |
Agree | 23 | 33.3 | 15 | 31.3 | 8 | 28.6 | |
0.605 | |||||||
Disagree | 14 | 18.2 | 9 | 18.4 | 5 | 17.9 | |
Agree | 63 | 81.8 | 40 | 81.6 | 23 | 82.1 | |
0.248 | |||||||
Disagree | 30 | 39.0 | 21 | 42.9 | 9 | 32.1 | |
Agree | 47 | 61.0 | 28 | 57.1 | 19 | 67.9 | |
0.165 | |||||||
Disagree | 14 | 18.2 | 11 | 22.5 | 3 | 10.7 | |
Agree | 63 | 81.8 | 38 | 77.6 | 25 | 89.3 | |
0.170 | |||||||
Disagree | 48 | 62.3 | 33 | 15 | 53.6 | ||
Agree | 29 | 37.7 | 16 | 13 | 46.4 | ||
0.145 | |||||||
Disagree | 35 | 45.5 | 25 | 10 | 35.7 | ||
Agree | 42 | 54.6 | 24 | 18 | 64.3 | ||
0.125 | |||||||
Disagree | 20 | 26.3 | 10 | 20.8 | 10 | 35.7 | |
Agree | 56 | 73.7 | 38 | 79.2 | 18 | 64.3 |
Note: The table shows the number and proportion of respondents ranking each statement as a facilitator (agree) versus barrier (disagree). It shows the results of the exploratory analysis comparing these same facilitator and barrier rankings amongst trainees and graduates, with
, Disagree is the combination of ‘strongly disagree’ and ‘disagree’ or ‘neither agree nor disagree’, whereas ‘agree’ is the combination of ‘strongly agree’ and ‘agree’.
,
,
This study exploratory analysis comparing reported research facilitators and barriers between trainees and graduates yielded no statistically significant differences. The biggest differences were seen in the degree to which other healthcare workers conduct research, with 38 out of 48 or 79.2% of trainees responding positively versus 18 out of 28 or 64.3% of graduates (
From the analysis of the open-ended responses, the researchers derived eight categories of barriers and six categories of facilitators of research activity. Each category, the number of times it was mentioned and representative quotes are found in Online Appendix 2. The study highlights the most commonly mentioned categories, which can be visualised along with specific responses in
Word cloud of most important facilitators.
Word cloud of most important barriers.
Facilitators reported by more than a quarter of respondents included the requirement to conduct research by one’s training programme or employer (23 respondents), positive influence of peers or other clinician-researchers (22 respondents), research mentorship (19 respondents) and the availability of research opportunities (19 respondents). The requirement to conduct research is exemplified by one participant here: ‘I’m a resident and I’m required to conduct an MMed research project’ (A1.43). Seventeen participants reported being facilitated by a personal drive to conduct research, such as this respondent: ‘The need to find solutions to problems faced in the course of practice’ (A1.35).
Two barriers were named by greater than a third of participants: (1) a lack of time, cited by 46 respondents, and (2) a lack of access to needed financial and other resources, cited by 32. The time constraint category included descriptions of a variety of competing demands on doctors’ time, as illustrated by one participant response:
‘Time. Teaching and attending to patients in the busy family medicine clinics as well as attending to family matters limit the time one has for research.’ (A1. 35)
Slightly fewer people (22) wrote about nonconducive research environments posing a barrier. The next most common barriers were a lack of research experience or training and a lack of proper mentors, both cited by 16. Some responses in the mentorship category acknowledge the interplay between a lack of time and mentorship:
‘The research mentor has other engagements like theatre sessions for emergency surgical procedures … so the research meeting with the mentor is usually cancelled.’ (A1.9-41)
The facilitators that were most common across both the open-ended and Likert scale responses were: (1) access to adequate research mentorship and (2) working in an environment where peers conduct research or where it was required by the institution to conduct research. A lack of time was the most common barrier across both open-ended and closed-ended responses. This included a lack of protected time at the workplace, as was asked about in the Likert scale question. Respondents in the open-ended questions also spoke of time barriers from work overload and conflicting family or workplace responsibilities. Another common barrier was the lack of training on how to conduct research analysis, with specific reference to qualitative analysis in the closed-ended questions and a wider array of gaps in research and analysis skills noted in the open-ended answers.
Predominant facilitators and barriers determined via triangulation.
Facilitators | Barriers |
---|---|
Working in an environment that requires or encourages research | Lack of time (from work overload, conflicting responsibilities or a lack of protected time from the workplace) |
Access to adequate research mentorship | Lack of training on how to conduct research analysis |
Note: The table shows the predominant facilitators and barriers that were supported by triangulating findings from both the open-ended responses and the Likert scale questions.
The researchers noted an inconsistency between the open-ended and closed-ended responses with regard to the access to research resources. In the closed-ended questions, the participants largely agreed that they had access to online databases and software to manage and analyse data and had access to an IRB. However, in the open-ended responses, 31 participants noted a lack of access to finances and resources (including electronic databases, analysis software and research support personnel) as barriers to conducting research.
In this study it was found that a relatively high level of research activity, with over 70% having participated in at least one research meeting, forum, chat or discussion group in the preceding month. Additionally, all but four participants indicated they planned to be involved in research in the future. The majority of the activity was skewed towards the earlier stages of the research process (meetings, mentorship, manuscript preparation) with only about a third of the total sample reporting having published a manuscript. Approximately two-thirds of the sample, however, reported that they were working on a publication. The main facilitators of research activity were systemic or organisational, such as research being a requirement at one’s training institution or job and having adequate mentorship. The main barriers, such as time constraints and a lack of research analysis skills, could be considered individual-based factors or systemic factors, depending on the context.
The relatively high degree of early-stage research activity in this study sample of early-career African FPs was heartening, given the large amount of literature citing research capacity gaps amongst primary care physicians, particularly in LMICs, and in SSA.
The finding that more respondents reported activity in the earlier stages of research, such as research meetings and mentorship, than they did later research dissemination activities, such as manuscript or abstract publications, also makes sense considering this study target population. Firstly, the majority of the sample were trainees who are at the beginning of both their clinical and research careers, so they may simply have not yet progressed to the publication phase yet. This is supported by the exploratory analysis which suggested that more graduates in this study sample had publications than did trainees. Amongst graduates in this study sample, however, only slightly more than half reported a research publication in the past three years. The same barriers to research activity identified via this study, such as time constraints and a lack of funding, are two of the common barriers to publication of research.
Encouraging amongst this study findings is that approximately 64% of respondents had one or more manuscripts under development for publication. These findings suggest that there is a need for targeted efforts to support young physician-researchers from an early stage to be successful in transforming this intention to publish into actual publications. An example of this would be a new approach by the
The observation in this study that the main facilitators of research activity were environmental and social, such as research being a requirement at one’s training institution or job and having adequate mentorship, makes sense given the characteristics of the target population. The AfriWon population is mostly drawn from organised training and residency centres across SSA, which would therefore provide the structure in which participants’ research activity is taking place. Factors such as access to research mentorship, access to electronic databases and software, access to an IRB, confidence in developing a protocol and confidence in undertaking a literature search were all predominately ranked as facilitators on the Likert scale questions in varying proportions. Amongst these a complex interplay can be seen between external and environmental facilitators and personal facilitators.
Similar research facilitators were reported by other authors. Pawar et al. found that 66% of respondents were motivated by a ‘guiding senior faculty member’, who would no doubt be providing mentorship.
Whilst access to research mentorship in this study population was ranked as a ‘facilitator’ by a slim majority (approximately 55%), this leaves a considerable number of respondents reporting not having mentors. This is important because the research capacity literature is very clear that research mentorship is a crucial aspect of HRCS.
In this study, a lack of time was observed as a clear research barrier, whilst other factors, such as research analytical skills and access to resources, were more nuanced. The main barrier of time constraints because of work overload, conflicting responsibilities and/or a lack of protected time was unsurprising given that the researchers were drawing from health workers in SSA, who are often reported to be in short supply and overworked.
Confidence in conducting qualitative research was a barrier reported in the main analysis, as well as amongst both trainees and graduates in this exploratory analysis. This suggests the barrier may remain stable throughout the career of the FPs, surviving the transition from trainee to fellow. The lack of confidence in qualitative analysis may therefore correspond with a competency not gained during residency. This analysis suggested, however, that graduates were more confident in quantitative analysis skills than trainees. This may indicate there may be a qualitative analysis-specific gap in this study target population and an opportunity for targeted HRCS in this area.
Also, it was noted that the majority of participants reported that they did have access to specific research tools, such as literature databases or software to manage and analyse data, but in the open-ended response questions, a considerable number of participants reported that they did not have access to these because of financial or other reasons. It might be that the qualitative data collection method was able to retrieve more nuanced information here. Further study is warranted to better understand the specific research resources for which this population has the greatest need.
A lack of time and a lack of adequate resources or research facilities are well-supported barriers in the literature in similar populations.
This study had a relatively large sample size of 77 participants, which compares well with literature.
Recruiting the participants online could be limited by response bias, because the questionnaire might have only drawn the attention primarily of respondents who are interested in research. The study tried to minimise this bias by offering a raffle for a primary care e-book relevant to all participants’ practice context. This, it was presumed, would increase the number of respondents and incentivise those without a specific interest in the topic. In addition, the study was unable to calculate the response rate of the study because there was no updated database of all AfriWon members from which a denominator could be drawn. Of note, via the AfriWon social media platforms, approximately 300 AfriWon members were accessed, but were unable to reach the large number of early-career FPs in the subregion who might have otherwise met the study inclusion criteria. This number is significant; a particular African country alone has approximately 1500 FP trainees.
More work is needed to identify the most promising strategies for addressing the specific barriers and capitalising on the facilitators identified. For example, whilst this study further confirmed that early-career FPs need more time for research, future work ought to explore the most effective way to provide this protected time. Similarly, further work could elucidate the best strategies for providing needed training on qualitative analysis to early-career FPs or how to better foster peer research networks. Another priority would be to develop a validated research activity tool that could be used for longitudinal evaluations of HRCS interventions designed to increase research activity in this population.
More interventions are needed to support early-career FPs to overcome the barriers faced and capitalise on existing facilitators. Evaluations of HRCS interventions aimed at this population should aim to account for the early steps in the research process and not limit measurement to the conventional ‘outcomes’ which are often narrowly focused on number of publications.
There is a population of young FP researchers who are committed to research careers and would benefit from targeted HRCS efforts to address the identified barriers of a lack of time and training. These efforts should build on enabling environments and expanding research mentorship in order to empower participants to reduce health challenges of SSA through primary care health research.
The authors would like to thank the AfriWon Research Collaborative (ARC) Advisory Group members Dr Christina Borba, Prof. Felicity Goodyear-Smith, Dr Brian Jack, Dr Aboi Madaki, Dr Martha Makwero, Prof. Bob Mash, Dr Keneilwe Motlhatlhedi, Dr Kwame Ayisi Boateng, Dr Sebaka Malope, Dr Denon Tshienda and Dr Nancy Scott for their guidance and support. We thank Dr Eve Slavich from Stats Central, Mark Wainwright Analytical Centre and Dr Peter Rockers for their inputs and guidance on our approach to the analysis. Lastly, we are grateful to all AfriWon Renaissance members who participated in the survey, especially those who reviewed and provided comments on our summarised results.
Chelsea M. McGuire, MD, MS is a principal investigator on a Consortium of Universities for Global Health (CUGH)’s Tom Hall Global Health Education grant that supported this project but did not fund her personally. Alexandra van Waes was supported by a grant by Boston University Undergraduate Research Opportunities Program.
P.O.A. contributed to the development of the data collection tool, helped with data collection, selected appropriate methodology, conducted all quantitative analysis, led and helped prepare the manuscript. C.M.M. secured funding for the project, developed the data collection tool, helped conduct content validity of the tool, helped with data collection, provided inputs to methodology, analysis, and results and helped prepare the manuscript. P.O.A and C.M.M. are co-first authors because of their equal contribution to this project. A.v.W. conducted data cleaning and management, conducted content analysis, developed manuscript figures, provided administrative support for manuscript preparation and helped prepare the manuscript. B.B.F. contributed to the development of the data collection tool, helped with data collection and contributed to and approved the final manuscript. F.L.-M. conducted content analysis and contributed to and approved the final manuscript. H.K. helped conduct content validity of the tool, helped with data cleaning, conducted content analysis and contributed to and approved the final manuscript. L.S.M. provided administrative support for manuscript preparation, identification of literature for review, formatting of tables and contributed to and approved the final manuscript. M.A.E.B. supported with citations for manuscript, provided administrative support for manuscript preparation and contributed to and approved the final manuscript. M.D. provided senior mentorship on data analysis and manuscript preparation and contributed to and approved the final manuscript. K.Y. helped secure funding for the project, contributed to the development of the data collection tool, helped conduct content validity of the tool, helped with data collection, provided inputs to methodology, analysis and results and contributed to and approved the final manuscript.
The production of this manuscript was supported by the Consortium of University for Global Health (CUGH)’s Tom Hall Global Health Education grant. Whilst working on the manuscript, C.M.M. was supported by the Family Medicine–General Internal Medicine–General Pediatrics Academic Fellowship Program at Boston University (T32HP10028) and Alexandra Van Waes was supported by a grant by Boston University Undergraduate Research Opportunities Program.
The data that support the findings of this study are not openly available because of the confidential nature of the data and are available from the corresponding author, P.O.A., upon reasonable request. Data are located in a password-protected online repository.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.