Predictors of Antenatal Care Service Utilization Among Women of Reproductive Age in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: This study aimed to provide pooled predictors of ANC (antenatal care) service use among women of reproductive age in Ethiopia. Methods: Studies were systematically searched using PubMed, Medline, CINAHL, EMBASE, and Google Scholar. The Newcastle–Ottawa scale (NOS) tool was utilized for quality assessment (risk of bias). All data analyses were conducted by utilizing Stata version 17. A random-effects model was used to obtain the pooled predictors of ANC use. The publication bias was checked using a funnel plot and Egger’s regression test. Results: Twenty-two studies with a sample size of 25,671 were included in this review. The identified predictors of ANC service use were highest wealth rank (AOR 1.92 [95% CI: 1.53–2.31]), formal women education (AOR 2.40 [95% CI: 1.75–3.06]), formal husband education (AOR 1.49 [95% CI: 1.36–1.66]), women age ≥ 20 (AOR 1.75 [95% CI: 1.47–2.17]), mass media exposure (AOR 1.44 [95% CI: 1.21–1.66]), good maternal knowledge about the pregnancy complication (AOR 1.49 [95% CI: 1.11–1.88]), planned pregnancy (AOR 1.59 [95% CI: 1.28–1.91]), women autonomy (AOR 1.42 [95% CI: 1.23–1.62]), and positive husband attitude about the ANC service use (AOR 2.63 [95% CI: 1.47–3.79]). Conclusions: Several predictors have increased the ANC service utilization, like wealth status, women’s and their husbands’ education, older/increasing women’s age, media exposure, maternal knowledge about pregnancy complications, planned pregnancy, women’s autonomy to decide on household health care, and positive husband attitude about the ANC service utilization.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".