The effectiveness of financial intervention strategies for reducing caesarean section rates: a systematic review
Bibliographic record
Abstract
BACKGROUND: The increasing trend of Caesarean section (CS) in childbirth has become a global public health challenge. Previous studies have proposed financial intervention strategies for reducing CS rates by limiting caesarean delivery on maternal request (CDMR). This study synthesizes such strategies while evaluating their effectiveness. METHODS: The sources of data for this study are Cochrane Library, PubMed, EMBASE, and CINAHL. The publication period included in this study is from January 1991 to November 2018. The financial intervention strategies are divide into two categories: healthcare provider interventions and patient interventions. Risk of Bias in Non-randomized Studies - of Interventions (ROBINS-I) was employed to assess the risk of bias of included studies. The outcome of each study was evaluated with Grades of Recommendation, Assessment, Development and Evaluation (GRADE) through the GRADEpro Guideline Development Tool software. RESULTS: Nine studies were included in this systematic review: five with high certainty evidence (HCE), three with moderate certainty evidence (MCE), and one with low certainty evidence (LCE). Of the nine studies, seven are centered on the effect of provider-side interventions. Three of the HCE studies found that the diagnosis-related group payment system, risk-adjusted capitation, and equalizing fee for both facilities and physicians were effective intervention strategies. One HCE and one MCE study showed that only equalizing facility fees between vaginal and CS deliveries in healthcare service settings had no significant effect on reducing the CS rate. The MCE study showed that case payment had a negative effect on reducing the CS rates. One LCE study revealed that the effect of a global budget system was uncertain, and one HCE and one MCE study focused on combining both provider and patient-side interventions. However, equalizing fees for vaginal and CS deliveries and a co-payment policy for CDMRs failed to reduce the CS rate. CONCLUSIONS: The effectiveness of risk-adjusted payment methods appears promising and should be the subject of further research. Financial interventions should consider stakeholders' characteristics, especially the personal interests of doctors. Finally, high-quality randomized control trials and comparative studies on different financial intervention methods are needed to confirm or refute previous studies' 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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".