Effective Coverage of Emergency Obstetric and Newborn Care Services in Africa: A Scoping Review
Bibliographic record
Abstract
Objective: This scoping review aimed to map the evidence of effective coverage (EC) of EmONC (Emergency Obstetric and Neonatal Care) services and associated factors in Africa. Methodology: The review used PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) checklist to select, appraise, and report the findings. We searched four databases (PubMed, Web of Science, Google Scholar, and Scopus) and grey literature published between Jan 01, 2011 - Dec 31, 2020. The search terms included "emergency", "obstetric", "newborn", "effective coverage", and "quality" with Boolean terms, AND and OR. The review was conducted using title, abstract, and full-article screenings. The results were analyzed thematically using NVivo v12 qualitative research data analysis software. Results: Of the 1811 searched studies, 32 met the eligibility criteria for review. The majority of the studies were from East (56.3%) and Western (28.1%) Africa. Most studies were cross-sectional, had targeted health facilities, and combined two or more data collection techniques. The thematic analysis yielded three themes: EmONC service utilization, quality of EmONC service, and factors associated with the quality of EmONC services. The review showed a scarcity of evidence and variations regarding the crude coverage, quality of care, and factors affecting the quality of EmONC services in Africa. Conclusion: The review reported that the utilization of EmONC services was below the WHO-recommended 100% in all studies, though some reported improvements over time. Disparities in EmONC services quality were paramount across studies and contexts. However, the methodological and analytical incongruity across studies brought difficulties in tracing and comparing the progress made in EmONC services utilizations. Registration: This scoping review protocol was first registered on the Open Science Framework (OSF) on Aug 27, 2021 (https://osf.io/khcte/).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".