The Prevalence of Physical Intimate Partner Violence During Pregnancy and the Postpartum Period: A Systematic Review With Implications for Probable Violence-Caused Brain Injury Among Child Bearers
Bibliographic record
Abstract
Intimate partner violence (IPV) persists as a cause of short-term, long-term, and chronic health consequences. The elevated risk of IPV during pregnancy and the postpartum period (P-IPV) is commonly attributed to increased demands for child bearers and intimate partners. P-IPV may impact the health of the child bearer, developing fetus, and post-birth child. The prevalence of physical P-IPV remains under-explored. The primary objective of the study is to describe the prevalence of physical P-IPV during the period from pregnancy through 24 months postpartum. Medline ( PubMed ), Embase , CINAHL , and PsycINFO were searched (2000–2023) using the PICO model, MeSH terms, and Boolean operators. Studies with intimate partners exposed to physical IPV during pregnancy and the postpartum period that described the prevalence of IPV were included. Fifty-five studies were included. The sample-weighted average prevalence of physical P-IPV was calculated as 14.7% (range 0.6%–52.4%, n = 55). The sample-weighted average prevalence of physical IPV during pregnancy was calculated as 4.4% (0.6%–42.5%, n = 48). The sample-weighted average prevalence of physical IPV during the postpartum period was calculated as 10.3% (2.2%–52.4%, n = 16). The prevalence of physical P-IPV remains a looming threat to child bearer, fetal, and early childhood health. Given the >80% prevalence of IPV-caused brain injury (IPV-BI) from physical IPV, brain injury is likely occurring during pregnancy and the postpartum period and must be considered. Further investigations should be undertaken to uncover the true prevalence and impact of BI during this timeframe and mitigate the risk of P-IPV.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".