Mental Health Correlates of Autism Spectrum Disorder in Gender Diverse Young People: Evidence from a Specialised Child and Adolescent Gender Clinic in Australia
Bibliographic record
Abstract
Research suggests an overrepresentation of autism spectrum diagnoses (ASD) or autistic traits in gender diverse samples, particularly in children and adolescents. Using data from the GENTLE (GENder identiTy Longitudinal Experience) Cohort at the Gender Diversity Service at the Perth Children’s Hospital, the primary objective of the current retrospective chart review was to explore psychopathology and quality of life in gender diverse children with co-occurring ASD relative to gender diverse children and adolescents without ASD. The Social Responsiveness Scale (Second Edition) generates a Diagnostic and Statistical Manual of Mental Disorders (DSM-5) score indicating a likely clinical ASD diagnosis, which was used to partition participants into two groups (indicated ASD, n = 19) (no ASD indicated, n = 60). Indicated ASD was far higher than would be expected compared to general population estimates. Indicated ASD on the Social Responsiveness Scale 2 (SRS 2) was also a significant predictor of Internalising behaviours (Anxious/Depressed, Withdrawn/Depressed, Somatic Complaints, Thought Problems subscales) on the Youth Self Report. Indicated ASD was also a significant predictor of scores on all subscales of the Paediatric Quality of Life Inventory. The current findings indicate that gender diverse children and adolescents with indicated ASD comprise an especially vulnerable group that are at marked risk of mental health difficulties, particularly internalising disorders, and poor quality of life outcomes. Services working with gender diverse young people should screen for ASD, and also provide pathways to appropriate care for the commonly associated mental health difficulties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".