The Effect of Performance-Based Financial Incentives on Improving Health Care Provision in Burundi: A Controlled Cohort Study
Bibliographic record
Abstract
To strengthen the health care delivery, the Burundian Government in collaboration with international NGOs piloted performance-based financing (PBF) in 2006. The health facilities were assigned - by using a simple matching method - to begin PBF scheme or to continue with the traditional input-based funding. Our objective was to analyse the effect of that PBF scheme on the quality of health services between 2006 and 2008. We conducted the analysis in 16 health facilities with PBF scheme and 13 health facilities without PBF scheme. We analysed the PBF effect by using 58 composite quality indicators of eight health services: Care management, outpatient care, maternity care, prenatal care, family planning, laboratory services, medicines management and materials management. The differences in quality improvement in the two groups of health facilities were performed applying descriptive statistics, a paired non-parametric Wilcoxon Signed Ranks test and a simple difference-in-difference approach at a significance level of 5%. We found an improvement of the quality of care in the PBF group and a significant deterioration in the non-PBF group in the same four health services: care management, outpatient care, maternity care, and prenatal care. The findings suggest a PBF effect of between 38 and 66 percentage points (p<0.001) in the quality scores of care management, outpatient care, prenatal care, and maternal care. We found no PBF effect on clinical support services: laboratory services, medicines management, and material management. The PBF scheme in Burundi contributed to the improvement of the health services that were strongly under the control of medical personnel (physicians and nurses) in a short time of two years. The clinical support services that did not significantly improved were strongly under the control of laboratory technicians, pharmacists and non-medical personnel.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".