Effects of different payment methods on perinatal care outcomes: a systematic review
Bibliographic record
Abstract
BACKGROUND: Respectful maternity care in healthcare facilities during childbirth is a growing concern around the world. It is more than just an important component of care quality; it is also a human right. The aim of this study was to develop and validate a tool to assess respectful maternity care practices among healthcare providers in Nepal. METHODS: We systematically searched primary studies published until May 2023, adhering to PRISMA guidelines. Studies evaluating causal effects of payment methods on perinatal outcomes were included. Quality was assessed using the JBI-MAStARI tools. Due to heterogeneity in the studies, a meta-analysis was not feasible; findings were summarized narratively and presented in tables/ figures. RESULTS: Fifty-three studies were included, focusing on prenatal care (62%), childbirth (28%), and NICU care (10%). Pay-for-performance (P4P) methods improved institutional deliveries and reduced costs but had minimal effects on prenatal care quality. Diagnosis-Related Groups (DRGs) methods reduced cesarean rates but increased complications compared to Fee-For-Service (FFS). Bundled payments lowered cesarean rates and costs, while blended methods also reduced rates but increased postpartum hemorrhage. In NICU care, DRG methods increased the length of stay (LoS) and costs, primarily due to upcoding. CONCLUSION: The effectiveness of payment methods depends heavily on their design, implementation, and context. Poorly designed programs, despite their potential, can lead to adverse outcomes. Further research is needed to develop effective and equitable payment models that sustainably enhance maternal and neonatal health outcomes.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".