Gender-Dysphoric Children and Adolescents: A Comparative Analysis of Demographic Characteristics and Behavioral Problems
Bibliographic record
Abstract
This study compared 358 children (mean age, 7.2 years) and 72 adolescents (mean age, 15.8 years) referred clinically for problems in their gender identity development with regard to demographic characteristics, behavioral problems, as measured by the Child Behavior Checklist (CBCL), and peer relations. Compared with the adolescent sample, the child sample had a greater proportion of males, had a higher mean IQ, was more likely to come from a higher social class background, was more likely to be living with both of their parents, was more likely to be Caucasian, was more likely to be born in Canada, and was more likely to speak English as a first language. The adolescent sample showed significantly more general behavioral disturbance on the CBCL than did the child sample although both age groups had, on average, mean scores that fell within the clinical range. The adolescent sample also had significantly poorer peer relations than the child sample, as judged by a three-item Peer Relations Scale derived from the CBCL. The differences in CBCL psychopathology generally remained significant even when controlling for the differences in demographics. The strongest predictor of CBCL psychopathology was that of the Peer Relations Scale. The role of both poor peer relations and the demographic variables in accounting for the CBCL psycho- pathology and with regard to gender identity differentiation is discussed.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".