A study on the implementation fidelity of the performance-based financing policy in Burkina Faso after 12 months
Bibliographic record
Abstract
BACKGROUND: Performance-based financing (PBF) in the health sector has recently gained momentum in low- and middle-income countries (LMICs) as one of the ways forward for achieving Universal Health Coverage. The major principle underlying PBF is that health centers are remunerated based on the quantity and quality of services they provide. PBF has been operating in Burkina Faso since 2011, and as a pilot project since 2014 in 15 health districts randomly assigned into four different models, before an eventual scale-up. Despite the need for expeditious documentation of the impact of PBF, caution is advised to avoid adopting hasty conclusions. Above all, it is crucial to understand why and how an impact is produced or not. Our implementation fidelity study approached this inquiry by comparing, after 12 months of operation, the activities implemented against what was planned initially and will make it possible later to establish links with the policy's impacts. METHODS: Our study compared, in 21 health centers from three health districts, the implementation of activities that were core to the process in terms of content, coverage, and temporality. Data were collected through document analysis, as well as from individual interviews and focus groups with key informants. RESULTS: In the first year of implementation, solid foundations were put in place for the intervention. Even so, implementation deficiencies and delays were observed with respect to certain performance auditing procedures, as well as in payments of PBF subsidies, which compromised the incentive-based rationale to some extent. CONCLUSION: Over next months, efforts should be made to adjust the intervention more closely to context and to the original planning.
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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 | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".