Intimate partner violence during pregnancy and adverse birth outcomes in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence is a significant public health issue that affects maternal and neonatal health worldwide. Several studies have been conducted to investigate the prevalence of intimate partner violence during pregnancy as well as the factors that contribute to it. As a result, the purpose of this study was to determine the impact of intimate partner violence on birth outcomes. METHODS: International databases including Scopus, PubMed, Google Scholar, Embase, and CINAHL were used to search primary studies. The quality and strength of the included studies were evaluated using the Newcastle-Ottawa Scale quality assessment tool. The studies heterogeneity and publication biases were assessed using I2 statistics and Egger's regression test. The Meta-analysis was carried out using STATA version 16 software. RESULTS: A total of nine hundred and fifty-eight articles were retrieved from various databases, and seventeen articles were included in the review. The pooled prevalence of intimate violence during pregnancy in Ethiopia was 32.23% (95% CI 28.02% -36.45%). During pregnancy, intimate partner violence was a significant predictor of low birth weight (AOR: 3.69, 95%CI 1.61-8.50) and preterm birth (AOR: 2.23, 95%CI 1.64-3.04). CONCLUSION: One in every three pregnant women experiences intimate partner violence. Women who experienced intimate partner violence during their pregnancy are more likely to experience adverse outcomes such as premature delivery and low birth weight infants.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| 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 | 0.002 |
| 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".