Towards Realizing the Human Right to Health Care for Rwandan Rural Communities through Community-Based Health Insurance (CBHI): The Case of Gisagara District
Bibliographic record
Abstract
This study is about the contribution of Community-Based Health Insurance Schemes (CBHI) to realizing the human right to health care in Rwanda. Through the example of Gisagara District, the study explores experiences of rural community households with the CBHI scheme in Rwanda. The study uses a mix of qualitative and quantitative approaches, drawing on interviews with twenty rural households, and a number of interviews with officials and health workers from the District and Ministry of Health. The key findings of this study were that the rate of enrolment is high, and this has helped many rural Rwandans access health care. However the study also found a challenge of sustainability, since around a quarter of rural households were found not to be enrolled, due to their limited financial means. One finding was that the local communities contribute over 65 per cent of all contributions, and donors and government only 35 per cent. For most rural people, although the CBHI system is compulsory, they support it. This includes those who cannot pay because they lack means; this suggests that CBHI seems to be genuinely viewed by those in rural areas as being of importance for their own access to health care. Another finding was that there was a strong political will on the part of government, aimed at improving the existing system. Some modest recommendations at the end of the study seek to improve levels of access in rural communities like Gisagara and elsewhere in Rwanda.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".