Improving emergency obstetric care at the primary health level: A capacity-building model in Lagos state, Nigeria
Bibliographic record
Abstract
Maternal mortality in Nigeria remains alarmingly high, with many deaths resulting from preventable obstetric complications. Primary Health Centers (PHCs), which serve as the first point of contact for most pregnant women, often lack the capacity to manage emergencies effectively. This manuscript examines a capacity-building model aimed at strengthening emergency obstetric care (EmOC) at the primary health level in Lagos State. Drawing from national initiatives and international best practices, the study explores training interventions, simulation exercises, and systemic improvements in supply chains and referral networks. Competency-based training significantly enhanced provider knowledge and confidence, especially when supported by simulation drills and supervision frameworks. Facility-based programs also led to increased use of uterotonics, better partograph documentation, and improved management of hypertensive emergencies. However, challenges such as staff attrition, limited monitoring systems, irregular supply of essential drugs, and fragmented referral coordination persist. The paper argues that sustainable EmOC improvements require policy integration, institutional ownership, and consistent funding. Recommendations include embedding EmOC modules in pre-service and in-service training for nurses and midwives, strengthening referral and transport systems, and ensuring logistical support across PHCs. The Lagos State experience underscores the importance of a systems-based, contextually adapted, and government-led approach to maternal health. If implemented broadly, such a model could accelerate progress toward reducing preventable maternal deaths and achieving Sustainable Development Goal 3.1 across Nigeria and other low-resource settings. Keywords: Maternal Mortality, Primary Health Care, Emergency Obstetrics 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".