Qualité du système d’information et de suivi des interventions en santé dans les zones exposées au financement basé sur les résultats en 2014 au Bénin
Bibliographic record
Abstract
INTRODUCTION: Performance-based financing (PBF) approach is a public health intervention, whose effects on the pillars of this system are often not measured, especially with regard to the information system and to the effectiveness of public health interventions. METHODS: Our cross-sectional study was conducted in Benin in 67 health units randomly drawn from two PBF_HSS (Health Systems Strengthening) health zones and two PBF_NHSSP (Health Sector Support Program) areas, all experiencing PBF, and from two areas where the PBF had not been implemented. It allowed to evaluate the quality of the information and the effectiveness of public health interventions. The quality index and the performance scores of the system components were used to compare the strata covered by the PBF and the noncovered strata. RESULTS: The quality of the information system and of the effectiveness of public health interventions was average in the three strata, with a higher quality index in the PBF_HSS (77%) and PBF_NHSSP (74%) strata than in the Non_PBF (67%) strata. Health system quality distribution was more favorable in the strata covered by PBF. The components achieving a good performance were "demographic information", "results and essential analyzes" and "statistic support archiving". However, the essential components of the PBF and of the information system were "supervision" and "reporting" that continued to have an average QI two years after the beginning of the intervention. CONCLUSION: The average quality of the information system and of the effectiveness of public health interventions could be improved by respecting the instructions of the PBF, especially when the quality of this system becomes a priority for the PBF.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".