Multidimensional determinants of willingness to pay for community-based health insurance in Ethiopia and its implication towards universal health coverage: A narrative synthesis
Bibliographic record
Abstract
Pooling resources to pay for healthcare services and attain universal health coverage is a viable global agenda, especially for underdeveloped health systems. Ethiopia has implemented community-based health insurance (CBHI) since 2011 to improve healthcare funding. However, comprehensive evidence on the demand and determinants of health insurance in Ethiopia is lacking. Therefore, this review aimed at identifying determinants of willingness to pay (WTP) for CBHI in Ethiopia. A narrative review was conducted using search terms from PubMed, Science Direct, Scopus, African Journal Online, and Google Scholar databases. Screening process considered publication year, settings, English language, and study participants. Newcastle Ottawa tool assessed the quality of included studies. A thematic framework was applied. The review protocol was registered in PROSPERO with an ID number CRD42022296840. The review included 10 studies. The synthesis identified 25 determinants of WTP for CBHI in Ethiopia. Socio-demographic and economic, scheme-related, and health-related determinants of WTP for the CBHI were identified. Determinants of household WTP for CBHI in Ethiopia were multi-dimensional. Socio-demographic, socio-economic, scheme-related, and health-related factors are among the common determinants documented. CBHI is thus an alternative and potential source of financing for the healthcare system, primarily for people with low socioeconomic status and a fragile health system. The health system, socioeconomic leaders, and political figures play a significant role in influencing communities towards WTP for CBHI while increasing government spending on health toward UHC.
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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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".