Influence of spoken language and gender identity on healthcare experiences of transgender and non-binary youth living in Quebec, Canada
Bibliographic record
Abstract
Whether spoken language influences experiences of trans and non-binary youth (TNBY) with healthcare systems is unknown. We analyzed Quebec data from the Canadian Trans and Non-Binary Youth Health Survey to illustrate healthcare experiences of predominantly French-speaking TNBY aged 14-25 and influence of gender identity and language on those experiences. We included 220 participants of whom 71% identified as French-speaking. Up to 78% reported a mental health problem and 51% reported foregoing mental health care in the last year. Only 26% of non-binary versus 57% of trans youth were comfortable discussing healthcare needs with providers (OR 0.26; 95% CI [0.13-0.54]). English youth were less likely than French youth to be comfortable discussing healthcare needs (aOR 0.33, 95% CI [0.13-0.83]. They were also more likely to forgo care because of negative experiences (aOR 2.21, 95% CI [1.00, 4.87]) and out of fear (aOR 2.38, 96% CI [1.08, 5.28]). Our study found that TNBY had a high prevalence of foregone health care despite a great need. In Quebec, a predominantly French-speaking area within Canada, language-minority English TNBY were less comfortable than French TNBY discussing healthcare needs and accessing needed resources. Limited availability of language-specific resources may be an additional barrier to healthcare access for TNBY.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".