No effects of pilot performance-based intervention implementation and withdrawal on the coverage of maternal and child health services in the Koulikoro region, Mali: an interrupted time series analysis
Bibliographic record
Abstract
Performance-based financing (PBF) has been promoted and increasingly implemented across low- and middle-income countries to increase the utilization and quality of primary health care. However, the evidence of the impact of PBF is mixed and varies substantially across settings. Thus, further rigorous investigation is needed to be able to draw broader conclusions about the effects of this health financing reform. We examined the effects of the implementation and subsequent withdrawal of the PBF pilot programme in the Koulikoro region of Mali on a range of relevant maternal and child health indicators targeted by the programme. We relied on a control interrupted time series design to examine the trend in maternal and child health service utilization rates prior to the PBF intervention, during its implementation and after its withdrawal in 26 intervention health centres. The results for these 26 intervention centres were compared with those for 95 control health centres, with an observation window that covered 27 quarters. Using a mixed-effects negative binomial model combined with a linear spline regression model and covariates adjustment, we found that neither the introduction nor the withdrawal of the pilot PBF programme bore a significant impact in the trend of maternal and child health service use indicators in the Koulikoro region of Mali. The absence of significant effects in the health facilities could be explained by the context, by the weaknesses in the intervention design and by the causal hypothesis and implementation. Further inquiry is required in order to provide policymakers and practitioners with vital information about the lack of effects detected by our quantitative analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".