Scaling-up health insurance through community- based health insurance schemes in rural sub- Saharan African communities
Bibliographic record
Abstract
Context: The knotty and monumental problem of health inequality and the high burden of diseases in sub-Saharan Africa bothers on the poor state of health of many of its citizens particularly in rural communities. These issues are further exacerbated by the harrowing conditions of health care delivery and the poor financing of health services in many of these communities. Against these backdrops, health policy makers in the region are not just concerned with improving peoples’ health but with protecting them against the financial costs of illness. What is important is the need to support more robust strategies for healthcare financing in these communities in sub-Saharan Africa. Objective: This review assesses the evidence of the extent to which community-based health insurance (CBHI) is a more viable option for health care financing amongst other health insurance schemes in rural communities in sub-Saharan Africa. Patterns of health insurance in sub-Saharan Africa: Theoretically, the basis for health insurance is that it allows for risk pooling and therefore ensures that resources follow sick individuals to seek health care when needed. As it were, there are different models such as social, private and CBHI schemes which could come to bear in different settings in the region. However, not all insurance schemes will come to bear in rural settings in the region. Community based health insurance: CBHI is now recognized as a community-initiative that is community friendly and has a wide reach in the informal sector especially if well designed. Experience from Rwanda, parts of Nigeria and other settings in the region indicate high acceptability but the challenge is that these schemes are still very new in the region. Recommendations and conclusion: Governments and international development partners in the region should collect- ively develop CBHI as it will help in strengthening health systems and efforts geared towards achieving the millennium development goals. This is because it is inextricably linked to the health care needs of the poor.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".