The role of family physicians in reducing maternal near-miss events: Lessons from a multi-facility review in Lagos
Bibliographic record
Abstract
Maternal near-miss events (instances in which women survive life-threatening obstetric complications) offer a valuable lens for evaluating the quality of maternal healthcare systems. In Nigeria, where maternal mortality remains unacceptably high, near-miss cases are often underreported and under analyzed. This paper explores the role of family physicians (FPs) in reducing maternal near-miss events through early identification, timely intervention, and coordinated care, drawing on insights from a multi-facility review in Lagos. The analysis highlights common clinical drivers of near-miss events, including postpartum hemorrhage, preeclampsia/eclampsia, and sepsis, often exacerbated by systemic delays in care. Family physicians are shown to mitigate these risks by introducing structured interventions such as routine use of partographs, maternal early warning systems, and emergency obstetric drills. They also lead community outreach programs aimed at improving birth preparedness and recognizing danger signs during pregnancy. Furthermore, their mentorship of non-physician staff and integration of team-based protocols enhance both facility readiness and care quality. The paper advocates for integrating maternal near-miss audits into family medicine residency curricula to strengthen quality improvement competencies. It also calls for expanded continuing education opportunities, peer-support structures, and policy frameworks that formally recognize FPs as essential contributors to maternal care. Finally, investments in rural infrastructure and referral capacity are recommended to support FPs working in underserved areas. By leveraging the unique skill set and placement of family physicians, Nigeria can better address systemic gaps in obstetric care, reduce preventable maternal morbidity, and advance toward its maternal health goals. Keywords: Maternal Near- Miss, Family Physician, Maternal Health, Primary Care.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".