Exposure to Interpersonal Violence During Pregnancy and Its Association With Women’s Prenatal Care Utilization: A Meta-Analytic Review
Bibliographic record
Abstract
Inadequate prenatal care utilization has been proposed as a mechanism between exposure to violence during pregnancy and adverse maternal and fetal obstetric outcomes. Adequate prenatal care is important for identifying and treating obstetric complications as they arise and connecting pregnant women to supports and interventions as needed. There is some evidence that pregnant women experiencing relational violence may delay or never enter prenatal care, though this association has not been systematically or quantitatively synthesized. The present meta-analysis investigates the relationship between interpersonal violence during pregnancy and inadequate prenatal care utilization across two dimensions: (1) no prenatal care during gestation ( k = 9) and (2) delayed entry into prenatal care ( k = 25). Studies were identified via comprehensive search of 9 social science and health-related databases and relevant reference lists. Studies were included if (1) participants were human, (2) violence occurred in the context of an interpersonal relationship, (3) abuse occurred during pregnancy (including abuse within 12 months before the time of assessment during pregnancy), (4) the study was empirical, peer-reviewed, and included quantitative data, (5) prenatal care utilization data were available, (6) they were in English, and (7) they were not part of an intervention study. Results from random-effects models found that women abused during pregnancy were more likely to never enter care (odds ratio [ OR] = 2.62, 95% confidence interval [CI] = [1.55, 4.42]) or to delay care ( OR = 1.81, 95% CI [1.48, 2.23]). Sociodemographic, abuse-related, and methodological factors emerged as moderators. Practice, policy, and research implications are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".