Analysis of the implementation of a social protection initiative to admit the poorest of the poor to mutual health funds in Burkina Faso
Bibliographic record
Abstract
Abstract To enable mutual health funds to extend coverage to poor people, the Mutual Health Support Network (Réseau d’appui aux mutuelles de santé – RAMS) in 2012 launched an initiative in collaboration with the Ministry of Social Action and Solidarity (ministère de l’Action sociale et de la Solidarité nationale – MASSN) in Burkina Faso. This article reveals difficulties in the initiative's implementation, which resulted in the continued exclusion of poor people from health services. Poor people were required not only to make co‐payments, but also to accept a limitation of coverage to three episodes of illness per year. Additional challenges to service takeup were the geographical distance of the homes of some beneficiaries covered by a mutual fund agreement from a health centre and the failure by some health workers and managers of pharmacies to recognize the mutual membership card. A formal framework was lacking that brought together all the actors involved in planning and implementing the initiative. Those involved did not all have the same information. Each structure performed the tasks within its scope, according to its own interests, but without consulting the other parties, and there was no platform for discussing implementation difficulties.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".