What About the Men? A Critical Review of Men’s Experiences of Intimate Partner Violence
Bibliographic record
Abstract
Intimate partner violence (IPV) is a health problem affecting people of all genders and other social locations. While IPV victimization of cis-gendered women has been widely researched, how men conceptualized or experience IPV victimization, and the variations in their experiences of IPV, has not been thoroughly examined. In this critical review of men's experiences of IPV, an extensive search of peer reviewed literature was conducted using multiple database (Cochrane database, MEDLINE, CINAHL, Embase, PsycgINFO, and Google Scholar) as well as the gray literature. We critically reviewed examining the conceptual foundations of IPV victimization among men. The influence or gender roles and societal expectation on men's experiences and perceptions of IPV victimization and their help-seeking behavior are explored. Current knowledge about types, tactics, and patterns of IPV against men and the health and social consequences of IPV are addresses. Additionally, the conceptual and empirical limitations of current research are discussed, including the tendency to compare only the prevalence rates of discrete incidents of abuse among women versus men; the use of IPV measures not designed to capture men's conceptualizations of IPV; and the lack of attention given to sex and gender identity of both the victim and perpetrator. Future research priorities that address these limitations and seek to strengthen and deepen knowledge about IPV among men are identified.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".