Social Support and Mental Health Among Transgender and Nonbinary Youth in Quebec
Bibliographic record
Abstract
Purpose:Transgender and nonbinary (TNB) youth face elevated levels of discrimination, stigma, mental health disorders, and suicidality when compared with their cisgender counterparts. Family and school support may mitigate some of the effects of the stressors facing TNB youth. This study aimed to better understand the impact of each of these sources of support on TNB youths' mental health and wellbeing. Methods:We used data collected between 2018 and 2019 as part of the Canadian Trans Youth Health Survey, a bilingual online survey to measure social support, physical health, and mental health in a sample of 220 TNB youth aged 14–25 living in Québec, Canada. We examined the relationships among different sources of support, and mental health and wellbeing outcomes using logistic regression. Analyses were conducted on the full sample and according to linguistic groups (French and English). Results:Participants reported high levels of mental health symptoms, self-harm, and suicidality, and mental health symptoms were higher in the English-speaking group (p = 0.005). In models controlling for age, family connectedness was associated with good/excellent self-reported mental health (odds ratio [OR] = 2.62, p = 0.001) and lower odds of having considered suicide (OR = 0.49, p = 0.003) or attempted suicide (OR = 0.43, p = 0.002), whereas school connectedness was associated with higher odds of good/very good/excellent general (OR = 2.42, p = 0.013) and good/excellent mental (OR = 2.45, p = 0.045) health. Conclusion:Family and school support present consistent associations with TNB youths' health and may constitute key areas for intervention for those supporting them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".