Contribution of the results‐based financing strategy to improving maternal and child health indicators in Burkina Faso
Bibliographic record
Abstract
In response to the poor performance of its public health care provision, Burkina Faso decided, to implement results-based financing (RBF). This strategy relies on a strategic purchase of the quantity and quality of services provided by health workers, monitored by a set of indicators. However, there is a lack of evidence on its effects. The objective of this article is to appreciate the effect of RBF on a set of maternal and child health (MCH) indicators in Burkina Faso. The study design is quasi-experimental comparative with a control group before and after the implementation of the RBF. To estimate the effect of RBF, we used two methods of analysis: (1) the segmented regression to measure the effect of RBF in the health districts (HD) implementing RBF (RBF HD) and (2) the difference-in-difference test to estimate the effect of RBF considering the differences in mean between RBF HD and HD that did not implement RBF (non-RBF HD). We found among five indicators studied that only the postnatal consultation coverage in RBF HD was significantly higher (7.68%; P = 0.04) than in the non-RBF HD. Overall, our findings do not clearly demonstrate the effectiveness of RBF in improving MCH indicators in Burkina Faso.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".