Out of hospital emergency care in Nigeria: A narrative review
Bibliographic record
Abstract
Introduction: Out of Hospital Emergency Care (OHEC) in Nigeria, the most populous country with the highest GDP in Africa, is considered inadequate. A better understanding of the current state of OHEC is essential to address the country's unique challenges and offer potential solutions. Objectives: This paper sought to identify gaps, barriers, and facilitators in implementing an OHEC model in Nigeria and provide recommendations for improvement. Methods: We searched MEDLINE (PubMed), Embase (OVID), CINAHL (EBSCO), and Google Scholar, using combinations of "emergency medical care" ('FRC,' 'PHC,' and 'EMS') OR prehospital care OR emergency training' AND 'Nigeria.' We included papers that described OHEC in Nigeria and were published in English. Of the initial 73 papers, those that met our inclusion criteria and those obtained after examination of reference lists comprised the 20 papers that contributed to our final review. Two authors independently reviewed all the papers, extracted data relevant to our objectives and performed a content analysis. All authors reviewed, discussed, and refined the proposed recommendations. Key recommendations: For OHEC to meet the needs of Nigerians and achieve international standards, the following challenges need to be addressed: harmful cultural practices, inadequate training of citizens in the provision of first aid or of professionals that provide prehospital care, lack of proper infrastructure, poor communication, absent policy, and poor funding. Based on the available literature, this paper proposes key recommendations to improve OHEC with the hope of improving the standards of living. The federal government should provide general oversight, but this will require political will on the part of the country's leadership and the provision of adequate funding.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".