Maternal Exposure to Domestic Violence and Pregnancy and Birth Outcomes: A Systematic Review and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Pregnant women who experience domestic violence are at increased risk of adverse outcomes in addition to the risks to themselves. Inadequate prenatal care, higher incidences of high-risk behaviors, direct physical trauma, stress, and neglect are postulated mechanisms. Our objective was to systematically review birth outcomes among women who experienced domestic violence. METHODS: Medline, Embase, CINAHL, and bibliographies of identified articles were searched for English language studies. Studies reporting rates of low birth weight, preterm birth, small for gestational age births, birth weight, or gestational age at birth were included. Study quality was assessed for selection, exposure assessment, confounder adjustment, analyses, outcomes assessment, and attrition biases. Unadjusted and adjusted data from included studies were extracted by two reviewers. Summary odds ratio (OR) and confidence intervals (CI) were calculated using the random effects model. Population-attributable risk was calculated. RESULTS: Thirty studies of low to moderate risk of biases were included. Low birth weight (adjusted OR 1.53, 95% CI 1.28-1.82) and preterm births (adjusted OR 1.46, 95% CI 1.27-1.67) were increased among women exposed to domestic violence. As the prevalence of reported domestic violence during pregnancy was low, the population-attributable risk was low. Prospective cohort studies provided robust and consistent results. CONCLUSIONS: Maternal exposure to domestic violence was associated with significantly increased risk of low birth weight and preterm birth. Underreporting of domestic violence is hypothesized. Effective programs to identify violence and intervene during pregnancy are essential.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".