The missing clinical guidance: a scoping review of care for autistic transgender and gender-diverse people
Bibliographic record
Abstract
The co-occurrence of autism and gender diversity has been increasingly studied in the past decade. It is estimated that ∼11% of transgender and gender-diverse (TGD) individuals are diagnosed with autism. However, there is insufficient knowledge about appropriate gender-related clinical care for autistic TGD individuals. We performed a scoping review of current clinical guidance for the care of TGD individuals to identify what was said about autism. Clinical guidance documents were searched in PubMed, Web of Science, Google Scholar, Embase, Guidelines International Network, and TRIP medical database, as well as reference mining and expert recommendation. Evidence was synthesised by narrative synthesis, recommendation mapping, and reference frequency analysis. Out of the identified 31 clinical guidance documents, only eleven specifically mentioned the intersection between autism and TGD. Key concepts among the available recommendations included advocating for a multidisciplinary approach; emphasising the intersectionality of autism and gender-diverse experiences during assessments; and-importantly-recognising that autism, in itself, does not serve as an exclusion criterion for receiving gender-related care. However, detailed and practical clinical guidance is lacking due to a gap in evidence. Empirical research into the care experiences and outcomes of autistic TGD individuals using a developmental, lifespan, and strengths-based approach is needed to generate evidence-informed and tailored guidance. Funding: This study was funded through a Canadian Institutes of Health Research Sex and Gender Science Chair program (GSB 171373) awarded to M-CL.
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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 | 0.045 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".