Proportional Overrepresentation of Gender-Diverse Identities in Two US-Based Autistic Adult Samples from the SPARK Database
Bibliographic record
Abstract
Background: Previous literature indicates a proportional overrepresentation of both autism and autistic traits within gender-diverse populations (individuals who experience their gender identity as different from their sex assigned at birth). Emerging but limited evidence also suggests a proportional overrepresentation of gender-diverse identities in autism. To our knowledge, this is the first study to report gender diversity prevalence in autistic adults in the United States. Methods: We report the prevalence of gender diversity within two well-characterized samples of autistic adults recruited from SPARK (Simons Foundation Powering Autism Research for Knowledge), the largest online research database of autistic individuals to date. This study includes both an original sample (Dataset 1, n = 205) and a replication sample (Dataset 2, n = 243). In addition, we looked at the co-occurrence of anxiety and/or mood disorders with gender-diverse identities. Results: We found that 16.1% of autistic adults in Dataset 1 and 19.8% of autistic adults in Dataset 2 were gender diverse. This compares with prior findings of 0.5% to 4.5% in the general adult population. Autistic participants who were gender diverse, compared with those who were not, were up to six times more likely to report diagnosed anxiety and/or mood disorder(s). The finding of proportional overrepresentation of gender diversity in autistic individuals is consistent with reports from other countries, and higher than some previous estimates (e.g., 15%). Conclusion: These findings point to the necessity for autism research to take gender identity into account in addition to sex assigned at birth, and to pay particular attention to the mental health challenges that gender-diverse autistic individuals may face. These important steps will lead toward increased understanding of the needs of gender-diverse autistic individuals and ways to improve care.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".