Health and Health Care Access of Autistic Transgender and Nonbinary People in Canada: A Cross-Sectional Study
Bibliographic record
Abstract
Background: The existence and health care needs of individuals who are both autistic and transgender and nonbinary (TNB) are increasingly discussed publicly. While research demonstrating a greater prevalence of autism among TNB individuals continues to grow, little captures their experiences with primary, mental health, and gender-affirming care (GAC), particularly between self-identified and diagnosed autistic TNB individuals. This article explores this nexus. Methods: = 176). We compared participant demographics, health status, and health care experiences by autistic status (diagnosed, self-identified only, or allistic [non-autistic]) using weighted chi-square tests and logistic regression analyses. Results: Of Trans PULSE participants, 14.3% were autistic (8.1% diagnosed, 6.2% self-identified). Compared with their allistic peers, autistic participants were younger, had lower levels of education, employment, and income, and were more likely to identify as asexual. They also reported worse overall general health, a higher rate of unmet health care needs, and significant mental health disparities. While few diagnosed (3.7%) or self-identified (1.1%) autistic participants reported being directly denied GAC due to autism, 25.5% of diagnosed and 36.1% of self-identified individuals preemptively avoided sharing information about it during GAC readiness assessments in the past year. Conclusions: Our findings highlight the need for changes to treatment of autistic TNB people in primary and mental health care. Future research should explore both individual responses and systematic changes to these challenges.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".