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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it