Prevalence, determinants, and trends in the experience and perpetration of intimate partner violence among a cohort of gay, bisexual, and other men who have sex with men in Montréal, Toronto, and Vancouver, Canada (2017-2022)
Bibliographic record
Abstract
Abstract Purpose Longitudinal data on the experience and perpetration of intimate partner violence (IPV) among gay, bisexual, and other men who have sex with men (GBM) is limited. We estimated the prevalence of past six-month (P6M) physical and/or sexual IPV (hereafter IPV) experience and perpetration, identified their determinants, and assessed temporal trends, including the impact of the COVID-19 pandemic. Methods We used data from the Engage Cohort Study (2017-2022) of GBM recruited using respondent-driven sampling in Montréal, Toronto, and Vancouver. Adjusted prevalence ratios (aPR) for determinants and self-reported P6M IPV were estimated using generalized estimating equations, accounting for attrition (inverse probability of censoring weights) and relevant covariates. Longitudinal trends of IPV were also assessed. Results Between 2017-2022, 1,455 partnered GBM (median age 32 years, 82% gay, and 71% white) had at least one follow-up visit. Baseline proportions were 31% for lifetime IPV experience and 17% for lifetime perpetration. During follow-up, P6M IPV experience was more common (6%, 95%CI: 5-7%) than perpetration (4%, 95%CI: 3-5%). Factors associated with P6M IPV experience include prior IPV experience (aPR=2.79, 95%CI: 1.83-4.27), less education (aPR=2.08, 95%CI: 1.14-3.79), and substance use (injection aPR=5.68, 95%CI: 2.92-11.54, non-injection aPR=1.70, 95%CI: 1.05-2.76). Similar factors were associated with IPV perpetration. IPV was stable over time; periods of COVID-19 restrictions were not associated with IPV changes in this cohort. Conclusion Prevalence of IPV was high among GBM. Determinants related to marginalization are associated with an increased risk of IPV. Interventions should address these determinants to reduce IPV and improve health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".