Incidence of and Risk Factors for Sexual Orientation–Related Physical Assault Among Young Men Who Have Sex With Men
Bibliographic record
Abstract
OBJECTIVES: We sought to determine incidence of, prevalence of, and risk factors for sexual orientation-related physical assault in young men who have sex with men (MSM). METHODS: We completed a prospective open cohort study of young MSM in Vancouver, British Columbia, surveyed annually between 1995 and 2004. Correlates of sexual orientation-related physical assault before enrollment were identified with logistic regression. Risk factors for incident assaults were determined with Cox regression. RESULTS: At enrollment, 84 (16%) of 521 MSM reported ever experiencing assault related to actual or perceived sexual orientation. Incidence was 2.3 per 100 person-years; cumulative incidence at 6-year follow-up was 10.8 per 100 person-years. Increased risk of incident sexual orientation-related physical assault was observed among MSM 23 years or younger (relative hazard=3.1; 95% confidence interval [CI] = 1.6, 5.8), Canadian Aboriginal people (relative hazard = 3.0; 95% CI=1.4, 6.2), and those who previously experienced such assault (relative hazard=2.5; 95% CI=1.3, 4.8). CONCLUSIONS: These data underscore the need for increased public awareness, surveillance, and support to reduce assault against young MSM. Such efforts should be coordinated at the community level to ensure that social norms dictate that such acts are unacceptable.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".