Gender diversity among adolescents with obesity in a weight management programme
Bibliographic record
Abstract
Gender dysphoria (GD) and obesity share commonalities, including associations with mental health comorbidities, disordered eating, body dissatisfaction and may intensify with physical and developmental changes during adolescence. While associations of obesity and gender diversity have been identified, rates of gender diversity among adolescents with obesity remain unclear. The aim was to examine gender diversity among adolescents with obesity in a weight management programme. A single-centre cross-sectional questionnaire study was conducted. Eligible adolescents received the Gender Identity/GD Questionnaire for Adolescents and Adults (GIDYQ-AA), a validated instrument measuring gender diversity and GD. Gender identities, sexual orientations, questionnaire scores, and frequency of GD (GIDYQ-AA score <3) were determined. The relationship of GIDYQ-AA scores and BMI Z-score (BMIz) was assessed. Of 72 consenting youth, 29 assigned females (AF) and 17 assigned males (AM) completed GIDYQ-AA and demographic questions. Seventeen (59%) AF reported non-heterosexual orientations, and 6 (21%) reported non-cisgender identities. One (6%) AM reported non-cisgender identity. Two (4%) AF individuals had GD based on GIDYQ-AA scores. GIDYQ-AA scores did not correlate with BMIz. In conclusion, adolescents with obesity, particularly AF with non-heterosexual orientation, reported high rates of non-cisgender identity and GD. Routine screening for gender-related concerns in weight management settings may be warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".