A Parent-report Gender Identity Questionnaire for Children: A Cross-national, Cross-clinic Comparative Analysis
Bibliographic record
Abstract
A one-factor, 14-item parent-report Gender Identity Questionnaire for Children (GIQC) was developed in a sample of 325 clinic-referred children with gender identity problems and 504 controls from Toronto, Canada (Johnson et al., 2004). In this study, we report a cross-national, cross-clinic comparative analysis of the GIQC on gender-referred children (N = 338) from Toronto and gender-referred children (N = 175) from Utrecht, The Netherlands. Across clinics, the results showed both similarities and differences. Gender-referred boys from Utrecht had a significantly higher total score (indicating more cross-gender behavior) than did gender-referred boys from Toronto, but there was no significant difference for girls. In the Toronto sample, the gender-referred girls had a significantly higher total score than the gender-referred boys, but there was no significant sex difference in the Utrecht sample. Across both clinics, gender-referred children who met the complete DSM criteria for gender identity disorder (GID) had a significantly higher cross-gender score than the gender-referred children who were subthreshold for GID (Cohen's d = 1.11). The results of this study provide the first empirical evidence of relative similarity in cross-gender behavior in a sample of gender-referred children from western Europe when compared to North American children. The results also provide some support for cross-clinic consistency in clinician-based diagnosis of GID.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".