Effects of results based financing models on the performance of exposed health zones in Benin
Bibliographic record
Abstract
Background: Since 2011, Benin adhered to results-based financing (RBF), with the implementation of RBF_PRPSS model by Health System Performance Strengthening Project (PRPSS) and RBF_PASS model by health system support project (PASS). Notwithstanding the lack of evidence on this experimental phase, the Ministry of Health initiated the extension of the RBF_PRPSS model to uncovered areas. This comparative study was led to evaluate the health system performance in RBF zones.Methods: The study examined data from sixty-seven health facilities in six health zones offering maternal and child health services, using the double difference, the Student's test and the variance comparison, with 5% significance level.Results: The study found that between 2011 and 2014, staff numbers remained stable in the RBF strata (p>0.05). The cumulative duration over a six-month period of stock-outs of five key drugs (paracetamol, amoxicillin, oxytocin, iron, sulfadoxine pyrimetamine) decreased from 51 days to 29 days (p<0.05). Direct revenues per health facility increased more in the RBF strata (p<0.05). Financial viability increased in RBF_PRPSS stratum. Health services utilization improved significantly for institutional delivery, tetanus toxoid immunization, DTP (Hib) HepB 3 and MCV immunization and curative care. Decreasing of maternal and neonatal mortalities in RBF strata were not significant.Conclusions: In sum, the RBF implementation has not yet generated a significant effect on the overall performance of the health system in exposed areas, although it is already accompanied by a significant improvement in the utilization of certain health care services.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".