Disparities in Suicide-Related Behaviors Across Sexual Orientations by Gender: A Retrospective Cohort Study Using Linked Health Administrative Data
Bibliographic record
Abstract
OBJECTIVE: The authors used a population-representative sample and health administrative data to quantify suicide-related behavior leading to acute care or deaths across self-identified heterosexual, gay/lesbian, and bisexual individuals. METHODS: Data from a population-based survey (N=123,995) were linked to health administrative data (2002-2019), and differences in time to suicide-related behavior events across sexual orientations were examined using Cox proportional hazards regression. RESULTS: The crude incidence rates of suicide-related behavior events per 100,000 person-years were 224.7 for heterosexuals, 664.7 for gay/lesbian individuals, and 5,911.9 for bisexual individuals. In fully adjusted (gender-combined) models, bisexual individuals were 2.98 times (95% CI=2.08-4.27) more likely to have an event, and gay men and lesbians 2.10 times (95% CI=1.18-3.71) more likely, compared with heterosexual individuals. CONCLUSIONS: In a large population-based sample of Ontario residents, using clinically relevant outcomes, the study found gay/lesbian and bisexual individuals to be at elevated risk of suicide-related behavior events. Increased education among psychiatric professionals is needed to improve awareness of and sensitivity to the elevated risk of suicide-related behavior among sexual minority individuals, and further research on interventions is needed to reduce such behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".