Intimate Partner Violence in Male Survivors of Child Maltreatment: A Meta-Analysis
Bibliographic record
Abstract
Intimate partner violence (IPV) is a major public health concern. Yet, despite an increasingly extensive literature on interpersonal violence, research on male victims of IPV remains sparse and the associations between different forms of child maltreatment (CM) and IPV victimization and perpetration in men remains unclear. The present meta-analysis evaluated five different forms of CM (sexual, physical, and psychological abuses, neglect, and witnessing IPV) as they predicted sexual, psychological, and physical IPV perpetration and victimization in men. Overall, most available studies examined men as perpetrators of IPV, whereas studies of victimization in men were relatively scarce. Results reveal an overall significant association ( r = .19) between CM and IPV. The magnitude of this effect did not vary as a function of type (perpetration vs. victimization) or form (sexual, psychological, or physical) of IPV. Although all forms of CM were related to IPV, with effect sizes ranging from .05 (neglect and IPV victimization) to .26 (psychological abuse and IPV victimization), these associations varied in magnitude according to the type of CM. Findings suggest the importance of expanding research on CM and IPV to include a range of different kinds of abuse and neglect and to raise concerns about the experience of men as both victims and perpetrators of IPV.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".