Improving Quality and Safety in Maternity Care: The Contribution of Midwife‐Led Care
Bibliographic record
Abstract
This article draws on findings from a recent Cochrane systematic review of midwife-led care and discusses its contribution to the safety and quality of women's care in the domains of safety, effectiveness, woman-centeredness, and efficiency. According to the Cochrane review, women who received models of midwife-led care were nearly eight times more likely to be attended at birth by a known midwife, were 21% less likely to experience fetal loss before 24 weeks' gestation, 19% less likely to have regional analgesia, 14% less likely to have instrumental birth, 18% less likely to have an episiotomy, and significantly more likely to have a spontaneous vaginal birth, initiate breastfeeding, and feel in control. In addition to normalizing and humanizing birth, the contribution of midwife-led care to the quality and safety of health care is substantial. The implications are that policymakers who wish to improve the quality and safety of maternal and infant care, particularly around normalizing and humanizing birth, should consider midwife-led models of care and how financing of midwife-led services can support this. Suggestions for future research include exploring why fetal loss is reduced under 24 weeks' gestation in midwife-led models of care, and ensuring that the effectiveness of midwife-led models of care on mothers' and infants' health and well-being are assessed in the longer postpartum period.
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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 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".