Gender diversity in a Chinese community sample and its associations with autism traits
Bibliographic record
Abstract
Emerging evidence suggests that gender dysphoria or gender diversity (GD) intersects frequently with autism spectrum disorder or autism traits. However, the magnitude and interpretation of this link continue to be debated. Most child studies on this topic were performed in clinical populations, and little is known about the generalizability of this co-occurrence to the broader community, especially to non-Western samples. Also, little is known about whether specific subdomains of autism are more strongly associated with GD. Therefore, we investigated GD and its association with autism traits in a Chinese community sample of 4-12-year-olds (N = 379; 51% birth-assigned girls). Parents provided information about GD characteristics using the standardized Gender Identity Questionnaire for Children and autism traits using the Chinese version of the Autism-Spectrum Quotient-Children. In addition, broader behavioral and emotional challenges were measured by the Behavior Problem Index (BPI) to account for psychological challenges other than autism traits. In this community sample of Chinese children, increased GD was associated with increased autism traits, even after accounting for the BPI. Of the four subscales, the Imagination and Patterns subscales in birth-assigned girls and the Imagination subscale in birth-assigned boys were especially associated with GD. These findings indicate that the association between GD and autism traits generalizes to a nonclinical, non-Western sample. Clinicians and researchers working with clinical as well as community children should thus pay attention to the co-occurrence of GD and autism traits, in and outside the West.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".