Childhood Gender Variance and the Autism Spectrum: Evidence of an Association Using a Child Behavior Checklist 10-Item Autism Screener
Bibliographic record
Abstract
Childhood gender variance (GV) is associated with autism spectrum disorder (ASD) diagnosis/traits; however, this association has mainly been investigated in clinical samples. An ASD screening measure based on 10 items from the commonly used Child Behavior Checklist (CBCL) might enable investigation of this association in a wider variety of (non-clinical) populations where the CBCL and a measure of GV are available. We investigated whether GV in 6- to 12-year-olds (N = 1719; 48.8% assigned male at birth) from a community sample showed an association with the CBCL 10-item ASD screener. The Gender Identity Questionnaire for Children measured GV. The CBCL 10-item ASD screener measured ASD traits. The remaining CBCL items provided a measure of children’s general emotional and behavioral challenges. Higher GV was associated with higher CBCL ASD screener scores, including when controlling for the remaining CBCL items. The CBCL 10-item ASD screener can be useful for investigating the link between GV and ASD traits in 6- to 12-year-olds. Given that the CBCL is commonly employed, secondary analyses of existing datasets that also included a measure of GV could enable investigation of how widely the association between GV and ASD applies across a variety of populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".