Effectiveness of the facility-based maternal near-miss case reviews in improving maternal and newborn quality of care in low-income and middle-income countries: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The maternal near-miss case review (NMCR) has been promoted by WHO as an approach to improve quality of care (QoC) at facility level. This systematic review synthesises evidence on the effectiveness of the NMCR on QoC and maternal and perinatal health outcomes in low-income and middle-income countries (LMICs). METHODS: Studies were searched for in six electronic databases (MEDLINE, Index Medicus, Web of Science, the Cochrane library, Embase, LILACS), with no language restrictions. Two authors independently screened papers and selected them for inclusion and independently extracted data. Maternal mortality was the primary outcome. Secondary outcomes included any outcome informing on any of the six dimensions of QoC: efficacy, safety, efficiency, equity, accessibility and timely care, acceptability and patient-centred care. RESULTS: =39%). A statistically significant reduction in the incidence of uterine rupture, postpartum haemorrhage and maternal sepsis was observed in three out of six studies. Ten studies reporting on maternal care process all showed some significant improvement when measured against predefined standards. All studies reported that the NMCR resulted in some amelioration of the facility structure (physical structure, staffing, equipment, training, organisation of care). Newborn outcomes were overall poorly reported; four studies showed no significant difference in perinatal mortality. Patient satisfaction and equity were also poorly reported. CONCLUSIONS: Policy makers may consider implementing the maternal NMCR cycle approach among strategies aiming at improving QoC and reducing maternal mortality and morbidity in LMIC. Future studies should better document the effectiveness of the NMCR cycle particularly on outcomes reflecting patient-centred care and cost-effectiveness.
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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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".