Behavior Problems and Psychiatric Diagnoses in Girls with Gender Identity Disorder: A Follow-Up Study
Bibliographic record
Abstract
This study evaluated the presence of clinical range behavior problems and psychiatric diagnoses in 25 girls referred for gender identity disorder (GID) in childhood (mean age: 8.88 years) at the time of follow-up in adolescence or adulthood (mean age: 23.2 years). At follow-up, three (12%) of the girls were judged to have persistent GID based on DSM-IV criteria. With regard to behavior problems at follow-up, 39.1% of the girls had a clinical range score on either the Child Behavior Checklist or Adult Behavior Checklist as rated by their mothers, and 33.3% had a clinical range score on either the Youth Self-Report or the Adult Self-Report. On either the Diagnostic Interview for Children and Adolescents or the Diagnostic Interview Schedule, the girls had, on average, 2.67 diagnoses (range: 0-10); 46% met criteria for three or more diagnoses. From the childhood assessment, five variables were significantly associated with a composite Psychopathology Index (PI) at follow-up: a lower IQ, living in a non-two-parent or reconstituted family, a composite behavior problem index, and poor peer relations. At follow-up, degree of concurrent homoeroticism and a composite index of gender dysphoria were both associated with the composite PI. Girls with GID show a psychiatric vulnerability at the time of follow-up in late adolescence or adulthood, although there was considerable variation in their general well-being.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".