Prevalence of Intimate Partner Violence Among Intimate Partners During the Perinatal Period: A Narrative Literature Review
Bibliographic record
Abstract
Intimate partner violence (IPV) affects individuals and families from all backgrounds, regardless of their ethnicity, socio-economic status, sexual orientation, or religion. Pregnancy and childbirth could be a time of vulnerability to violence because of changes in physical, emotional, social, and economic demands and needs. Prevalence of IPV against women during the perinatal period is increasingly researched and documented. However, evidence on IPV prevalence among intimate partners as well as on the course of IPV over the perinatal period is scarce. The purpose of this review was to provide a narrative synthesis of the existing literature regarding the prevalence estimates of IPV among intimate partners over the perinatal period. Through this review, we also gained better insight into associated factors, as well as the various forms of IPV. Of the 766 studies assessing prevalence estimates identified, 86 were included, where 80 studies focused on unidirectional IPV (i.e., perpetrated by men against women) and six studies investigated bidirectional IPV (i.e., IPV perpetrated by both partners). Most of the included studies reported lower overall prevalence rates for unidirectional IPV postpartum (range: 2-58%) compared to pregnancy (range: 1.5-66.9%). Psychological violence was found to be the most prevalent form of violence during the entire perinatal period. Studies on bidirectional IPV mostly reported women's perpetration to be almost as high as that of their partner or even higher, yet their findings need to be interpreted with caution. In addition, our results also highlighted the associated factors of IPV among partners, in which they were assimilated into a multi-level ecological model and were analyzed through an intersectional framework. Based on our findings, IPV is found to be highly prevalent during the entire perinatal period and in populations suffering from social inequalities. Further research exploring not only the occurrence, but also the motivations and the context of the bidirectionality of IPV during the perinatal period may facilitate better understanding of the detrimental consequences on partners and their families, as well as the development of effective intervention strategies. Public health prevention approaches intervening at optimal times during the perinatal period are also needed.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".