Intersectional analysis of suicide risk among transgender and non-binary people
Bibliographic record
Abstract
Transgender and non-binary (TNB) people experience high rates of suicidal thoughts and behaviors, but less is known about how suicide risk varies within TNB communities. We investigated variation in suicidality among TNB people across intersecting social identities and positions. This study uses data from Trans PULSE Canada, a 2019 community-based survey of TNB people aged 14+ in Canada. Among 2054 participants aged 16+, conditional inference trees (CTREE) were used to identify subgroups with varying levels of past-year suicidal ideation and suicide attempts. Twelve predictor variables were chosen through literature review and community knowledge based on their relevance to suicide risk. Findings show that over the past year, 30.2 % of participants reported suicidal ideation and 4.2 % reported a suicide attempt. Six subgroups with varying levels of past-year suicidal ideation were identified, with a higher prevalence of ideation among those with lower educational attainment (particularly youth aged 16–24; 49.4 %) and among college-educated participants who identified as disabled (38.3 %). The CTREE for past-year suicide attempts identified variation across three subgroups: participants aged 16–19 (11.0 % attempted), and those aged 20+ with or without a history of sex work (8.4 % and 2.5 %, respectively). In conclusion, past-year suicidal ideation and attempts were high compared to general population estimates, although somewhat lower than in previous Canadian TNB studies. Suicide attempts were concentrated among those aged 16–19, and amongst those 20+ with a history of sex work. Suicide prevention research should investigate individual and structural level interventions to reduce disproportionate suicide risk among these groups.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".