Exploring the role of social representations in micro-health insurance scheme enrolment and retainment in sub-Saharan Africa: a scoping review
Bibliographic record
Abstract
Low enrolment in micro-health insurance (MHI) schemes is a recurring issue affecting the viability of such schemes. Beyond the efforts addressing low subscription and retention in these schemes, little is known on how social representations are related to micro-health insurance schemes enrolment and retention. This scoping review aimed at exploring the role of social representations in shaping enrollment and retention in MHI in sub-Saharan Africa. We reviewed qualitative, quantitative and mixed methods studies conducted between 2004 and 2019 in sub-Saharan Africa. We limited our search to peer-reviewed and grey literature in English and French reporting on social representations of MHI. We defined social representations as conventions, cultural and religious beliefs, local rules and norms, local solidarity practices, political landscape and social cohesion. We applied the framework developed by Arksey and O'Malley and modified by Levac et al. to identify and extract data from relevant studies. We extracted information from a total of 78 studies written in English (60%) and in French (40%) of which 56% were conducted in West Africa. More than half of all studies explored either cultural and religious beliefs (56%) or social conventions (55%) whereas only 37% focused on social cohesion (37%). Only six papers (8%) touched upon all six categories of social representation considered in this study whereas 25% of the papers studied more than three categories. We found that all the studied social representations influence enrollment and retention in MHI schemes. Our findings highlight the paucity of evidence on social representations in relation to MHI schemes. This initial attempt to compile evidence on social representations invites more research on the role those social representations play on the viability of MHI schemes. Our findings call for program design and implementation strategies to consider and adjust to local social representations in order to enhance scheme attractiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".