What we know and don’t know about the antenatal care service utilization in Ethiopia: A scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: In Ethiopia, there has been considerable recent investment and prioritization in the maternal health program. However, coverage rates have been low and stagnant for a long time, indicating the existence of systemic utilization barriers. Therefore, it is fundamental to synthesize the current body of knowledge to successfully address these problems and enhance program effectiveness to increase antenatal care (ANC) uptake. METHODS: We conducted a scoping review of the literature. Using various combinations of search strategies, we searched Pubmed/Medline, WHO Library, ScienceDirect, Cochrane Library, Google Scholar, and Google for this review. Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) were used to conduct the review. We included studies that used any study design, data collection, and analysis methods related to antenatal care utilization. RESULTS: A total of 76 studies, national surveys, and estimates were included in this review. The analysis revealed that ANC utilization coverage varied considerably by region, from 27% in Somali to 90.6% in the Oromia region, with significant disparities in socioeconomic status, access to healthcare, and vaccination knowledge. Ten priority research areas covering various aspects of the national ANC services were identified through a comprehensive review of the existing body of knowledge led by experts using the Delphi method. CONCLUSION: The barriers to recommended ANC service utilization differ depending on the context, suggesting that evidence-based, locally customized interventions must be developed and implemented. This review also identified evidence gaps, focusing on health system-related utilization barriers at the lower level, and identified additional research priorities in Ethiopia's ANC service. The first step in developing and executing targeted program approaches could be identifying coverage of ANC services utilization among those with disadvantages.
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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 | 0.026 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".