Implementing performance-based financing in peripheral health centres in Mali: what can we learn from it?
Bibliographic record
Abstract
INTRODUCTION: Numerous sub-Saharan African countries have experimented with performance-based financing (PBF) with the goal of improving health system performance. To date, few articles have examined the implementation of this type of complex intervention in Francophone West Africa. This qualitative research aims to understand the process of implementing a PBF pilot project in Mali's Koulikoro region. METHOD: We conducted a contrasted multiple case study of performance in 12 community health centres in three districts. We collected 161 semi-structured interviews, 69 informal interviews and 96 non-participant observation sessions. Data collection and analysis were guided by the Consolidated Framework for Implementation Research adapted to the research topic and local context. RESULTS: Our analysis revealed that the internal context of the PBF implementation played a key role in the process. High-performing centres exercised leadership and commitment more strongly than low-performing ones. These two characteristics were associated with taking initiatives to promote PBF implementation and strengthening team spirit. Information regarding the intervention was best appropriated by qualified health professionals. However, the limited duration of the implementation did not allow for the emergence of networks or champions. The enthusiasm initially generated by PBF quickly dissipated, mainly due to delays in the implementation schedule and the payment modalities. CONCLUSION: PBF is a complex intervention in which many actors intervene in diverse contexts. The initial level of performance and the internal and external contexts of primary healthcare facilities influence the implementation of PBF. Future work in this area would benefit from an interdisciplinary approach combining public health and anthropology to better understand such an intervention. The deductive-inductive approach must be the stepping-stone of such a methodological approach.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".