Intersectionality, gender affirmation, social support, and suicidality among transgender and non-binary people in Canada
Bibliographic record
Abstract
Transgender and non-binary (TNB) people experience higher levels of suicidal ideation and suicide attempts compared to the general population. Minority stress, or stress related to gender minority identity and gender non-affirmation, may contribute to these heightened levels. Intersectional perspectives show us that suicide risk may vary across groups of TNB people based on one's social identity and positions (e.g., race, education level, etc.). Some factors such as medical gender affirmation and individual types of social support have been shown to be protective against suicide among TNB people, but the associations between social and legal gender affirmation and the cumulative effects of social support with suicide have been less explored. This dissertation used data from Trans PULSE Canada -- a 2019 national, community- based research project that gathered information on the health of TNB people living in Canada. First, we identified inequalities in suicide risk based on the intersections of social and demographic positions. Younger participants (aged 16-24), those with lower educational attainment, and those with a history of sex work, had elevated rates of suicidal ideation and attempts. Second, we evaluated the associations between legal and social gender affirmation and past-year suicidal ideation and attempts. We found that social gender affirmation (being misgendered less often) and legal name change were associated with less past-year suicidal ideation, but neither social nor legal gender affirmation were associated with suicide attempts. Finally, the third aim identified latent classes based on levels of social support and evaluated their associations with past-year suicidality. Five latent classes were identified: a class of participants that had lower levels of all types of social support and were at higher risk of suicidality, a class with higher levels of all types of support and lowered risk of suicidality, and classes who each had lower levels of parental support and emotional and tangible support, or no romantic partner. The results of this dissertation inform the development of individual and structural level interventions that are tailored to groups such as TNB youth and sex workers, or that emphasize the importance of correct name/pronoun usage and the support needed from TNB friends and family members.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".