Disproportionate mental health risks in autistic females: A rapid review with quantitative and narrative syntheses
Bibliographic record
Abstract
Mental health conditions are highly prevalent among autistic people, but an updated synthesis of sex-stratified prevalence data, contributing factors, and support strategies is lacking.To address this knowledge gap, we conducted a rapid review utilizing PRISMA-ScR guidelines. MEDLINE, CINAHL, and PsycINFO databases were searched for studies (2004-2024) including female participants with a clinical autism diagnosis, and with a focus on mental health. Of 8,420 records screened, 218 met inclusion criteria. An exploratory quantitative synthesis of population-based and registry-based studies revealed higher rates of mental health conditions in autistic females than males for anxiety, mood, eating, obsessive-compulsive, psychotic, and personality disorders. Narrative synthesis identified moderating factors, including sex-related physiology, gendered experiences, age, age at autism diagnosis, autism characteristics, and co-occurring conditions. Biological and social mechanisms likely interact as contributing factors. Severe consequences of poor mental health underscore the need for tailored approaches accounting for the specific profiles of autistic females.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".