A systematic review of the effect of performance-based financing interventions on out-of-pocket expenses to improve access to, and the utilization of, maternal health services across health sectors in sub-Saharan Africa
Bibliographic record
Abstract
Background: Performance-based financing (PBF) assumes that subsidizing user fees for maternal health services to reduce out-of-pocket expenses will expand coverage and reduce inequities in access to maternal health services. It is usually associated with process changes, and the idea that increasing a facility's resources from PBF interventions can improve the availability of equipment, drugs, and medical supplies at the facility, has an indirect effect on out-of-pocket expenses. Assessment of complex interventions such as PBF requires consideration of specific underlying assumption or theories of change. Such assessment will allow a better and broader understanding of the system's strengths and weaknesses, where the gaps lie, whether the theory of change is sound, and will inform policy design and implementation. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) checklist, we performed a systematic review and a critical appraisal of selected studies using the risk-of-bias criteria developed by the Cochrane Effective Practice and Organisation of Care. We used the Grading of Recommendation and Evaluation, Development and Assessment framework for assessing the overall strength of the evidence. Results: After the abstract screening (n = 9873), we deemed 302 as relevant for full-text screening and assessed 85 studies for review eligibility. Finally, we included 17 studies in the review. We could not conduct a meta-analysis, so we report a narrative synthesis. As an add-on to an existing payment mechanism, PBF may facilitate the removal of operational barriers to enhance utilization of certain maternal health services in some contexts, especially in public facilities. Conclusions: PBF strategies may potentially decrease out-of-pocket expenses for specific maternal health services, especially in settings that have already instituted some form of user fee exemption policies on maternal health services. The implementation of PBF can be considered a potential access instrument in reducing out-of-pocket expenses to stimulate demand for maternal services. However, the implementation approaches employed will determine utilization, taking into consideration existing equitable and inequitable access characteristics which vary by context. Registration: PROSPERO CRD42020222893.
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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 | 0.021 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".