Medical and cosmetic intervention needs, priorities and barriers of trans and non-binary youth in Quebec
Bibliographic record
Abstract
Trans and non-binary (TNB) youth aged 12–17 and 16–25 represent 0.2% and 0.79% of the Canadian population respectively, likely underestimated. TNB youth face mental health challenges, sometimes related to gender dysphoria, which can be significantly improved with gender-affirming interventions. However, the needs of TNB youth are poorly documented in Quebec (Canada). This study aims to understand the gender-affirming intervention needs and desires of TNB youth in Quebec. An online survey was conducted between April and June 2023, including open-ended questions. Descriptive analyses were performed. A total of 84 TNB youth from Quebec aged 15–24 completed the survey (40% transmasculine, 20% transfeminine and 39% non-binary). The most desired intervention was hormone therapy (95%). We found gendered differences in needs, particularly for facial and upper/lower body interventions. Our findings suggest that the needs of TNB youth vary according to gender. Inequitable financial barriers persist in covered gender-affirming medical care (GAMC), disadvantaging transfeminine youth. Our data also highlighted the importance of hormone treatments for TNB youth. In conclusion, it is essential to support TNB youth by considering their needs for GAMC, offering information on all available interventions, and ensuring equitable coverage to GAMC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".