Self-Perceived Reasons for Suicide Attempts in Sexual and Gender Minorities in Canada
Bibliographic record
Abstract
The aim of this study was to examine the self-perceived reasons of suicide attempts among sexual and gender minorities (SGM). We surveyed SGM living in Canada (n = 2778) and respondents who had attempted suicide answered open-ended questions about their perceived reason(s) of their first/only attempt (FOA) and last attempt (LA) (for those who attempted multiple times). Responses were double-coded and categorized as discrete findings. A quarter (25%, n = 695) of the total sample reported a history of suicide attempt, of whom 72% reported multiple attempts. Respondents described a wide variety of reasons for their suicide attempts, with an important number of individuals reporting multiple reasons (corresponding to 47.5% of FOA and 43% of LA). Emotional issues (FOA:42.1%, LA:44.0%) were the most prevalent category of reasons for suicide attempts followed by experience of mental illness (FOA:30.1%, LA:36.1%). Other common reasons included violence (FOA:23.2%, LA:10.2%), interpersonal conflict (FOA:13.4%, LA:6.0%), stress related to life circumstances (FOA:9.5%, LA:16.7%), relationship issues (FOA:7.9%, LA:13.3%), and minority stress related to sexuality (FOA:11.1%, LA:6.2%) and gender identity (FOA:5.0%, LA:6.8%). SGM assessments of the reasons underlying their suicide attempts yielded a variety of factors, many of which were absent from the literature on SGM suicide but amenable to tailored interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".