Severity of Victimization and Formal Help Seeking Among Men Who Experienced Intimate Partner Violence in Their Ongoing Relationships
Bibliographic record
Abstract
This study examined the severity of intimate partner violence (IPV) experienced by men in their ongoing relationships and their help-seeking behavior with the criminal justice system and other professional agencies. This study also examined the extent to which socio-demographic variables predicted formal help seeking among male victims of IPV. Data were drawn from 2009 and 2014 cycles of the Canadian General Social Survey on Victimization with a nationally representative sample of 52,400 respondents. It is estimated that about 655,400 men in Canada reported having experienced physical and/or sexual victimization due to IPV in married/common-law relationships at the time of the surveys between 2004 and 2014. The latent class analysis generated four types of IPV victimization among men. Among male victims of physical and/or sexual IPV, about 64,000 men experienced the most severe type of IPV characterized by chronic and severe physical and psychological violence with a high probability of injuries and negative emotional effects of IPV. Although most of the male victims of IPV did not seek formal help (e.g., did not contact the police and IPV services), the severity of experienced violence was associated with the increased use of formal services. Some structural factors, such as being unemployed and residing with young children, were found to be substantial barriers to contacting formal agencies for help. Our findings highlight the need for the development of gender-inclusive and gender-sensitive public policy and intervention programs that help all victims of IPV regardless of victim gender.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".