The Role of Health Policy and Systems in the Uptake of Community-Based Health Insurance Schemes in Low- and Middle-Income Countries: A Narrative Review
Bibliographic record
Abstract
This study explores how health policies and systems can affect voluntary uptake of community-based health insurance (CBHI) schemes in low- and middle-income countries (LMICs). A narrative review was conducted involving searches of 10 databases (Medline, Global Index Medicus, Cumulative Index to Nursing, and Allied Health Literature, Health Systems Evidence, Worldwide Political Science Abstracts, PsycINFO, International Bibliography of the Social Sciences, EconLit, Bibliography of Asian Studies, and Africa Wide Information) across the social sciences, economics, and medical sciences. A total of 8107 articles were identified through the database searches, 12 of which were retained for analysis and narrative synthesis after 2 stages of screening. Our findings suggest that in the absence of directly subsidizing CBHI schemes by governments in LMICs, government policies can nonetheless promote voluntary uptake of CBHIs through intentional actions in 3 key areas: (a) improving quality of care, (b) providing a regulatory framework that integrates CBHIs into the national health system and its goals, and (c) leveraging administrative and managerial capacity to facilitate enrollment. The findings of this study highlight several considerations for CBHI planners and governments in LMICs to promote voluntary enrollment in CBHIs. Governments can effectively extend their outreach toward marginalized and vulnerable populations that are excluded from social protection by formulating supportive regulatory, policy, and administrative provisions that enhance voluntary uptake of CBHI schemes.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".