Analyse de la qualité de mise en œuvre et de la pérennité des modèles de Financement Basé sur les Résultats au Bénin en 2015
Bibliographic record
Abstract
Results-based financing (RBF) is described as an intervention that generates significant results in health services. The lack of evidence in Benin since its adoption led this study to analyze, via functions supported by institutional framework, the quality of implementation and the sustainability characteristics of the two RBF models used in the country. The study was carried out on 2015 in two zones covered by the RBF_PRPSS model developed by the Health System Performance Strengthening Project (PRPSS) and two others exposed to the RBF_PASS model implemented by the Health System Support Project (PASS). The data collected by documentary review and interview were used to determine the quality index of the RBF implementation. The two models, whose structures implicitly integrated five functions, presented at the start a weak functionality of the health facilities and an irregularity and a low quality of the regulation. Three years after its start, the quality of RBF implementation in Benin was below average, with an index of 52.5% (± 5%) in the RBF_PRPSS and 45.5% (± 5%) in the RBF_PASS. The functions with the lowest scores in both models were regulation, delivery and verification. In sum, reactivity and efficiency were low in RBF_PRPSS while reactivity and independence were low in RBF_PASS. The low quality of implementation of the two RBF models and the uncertainty of the financing, concomitant with the delay of evidence, hypothecate their durability and incite to design a harmonized and adapted model for the country.
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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 | 0.037 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".