Bridging the gap between primary care and specialist obstetrics: A collaborative model for managing high-risk pregnancies in rural Nigeria
Bibliographic record
Abstract
High-risk pregnancies contribute significantly to maternal morbidity and mortality in rural Nigeria due to fragmented care and weak referral systems. Primary health centers often lack the resources, training, and specialist linkages needed to manage obstetric emergencies effectively. This manuscript proposes a collaborative care model that strengthens the interface between primary care providers and specialist obstetric services to improve outcomes for high-risk pregnancies in rural settings. We describe a theoretical pilot model involving structured task-sharing, digital triage platforms, and referral coordination through designated family physicians. Key components include WhatsApp-based consultation groups, standardized referral protocols, and quarterly case review meetings between primary and tertiary providers. The model outlines measurable outcomes such as improved referral accuracy, reduced time to definitive care, and enhanced provider confidence. It also identifies implementation challenges including transport delays, network connectivity issues, and role ambiguity among providers. Sustainability requires ongoing training, alignment with national maternal health policies, investment in rural infrastructure, and workforce retention strategies. Embedding such models into existing health systems could bridge critical gaps and reduce preventable maternal deaths. A scalable, collaborative model integrating primary and specialist obstetric care holds promise for addressing systemic delays and improving maternal outcomes in rural Nigeria. Institutional commitment and policy alignment are essential for successful implementation. Keywords: High Risk Pregnancy, Maternal Mortality, Family Physicians, 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".