Intimate Partner Violence Against Women Before, During, and After Pregnancy: A Meta-Analysis
Bibliographic record
Abstract
Intimate partner violence (IPV) against pregnant women negatively impacts women's and infants' health. Yet inconsistent results have been found regarding whether pregnancy increases or decreases the risk of IPV. To answer this question, we systematically searched for studies that provided data on IPV against women before pregnancy, during pregnancy, and after childbirth. Nineteen studies met our selection criteria. We meta-analyzed the nineteen studies for the pooled prevalence of IPV across the three periods and examined study characteristics that moderate the prevalence. Results showed the pooled prevalence estimates of IPV were 21.2% before pregnancy, 12.8% during pregnancy and 14.7% after childbirth. Although these findings suggest a reduction in IPV during pregnancy, our closer evaluation of the prevalence of IPV after childbirth revealed that the reduction does not appear to persist. The prevalence of IPV increased from 12.8% within the first year after childbirth to 24.0% beyond the first year. Taken together, we should not assume pregnancy protects women from IPV, as IPV tends to persist across a longer-term period. Future studies are needed to investigate if IPV transits into other less obvious types of violence during pregnancy. Moderator analyses showed the prevalence estimates significantly varied across countries by income levels and regions.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".