Associations Between Gender Diversity and Eating Disorder Symptoms in Early Adolescence
Bibliographic record
Abstract
OBJECTIVE: To assess associations between multiple dimensions of gender diversity with eating disorder symptoms in a national cohort of U.S. early adolescents. METHOD: = 12.9 years, 2019-2021). Gender diversity was measured using multiple dimensions, including categorical gender identity (e.g., transgender, cisgender), categorical and continuous felt gender (congruence between gender identity and assigned sex), ordinal gender non-contentedness (dissatisfaction with one's gender), and ordinal gender expression (communication of gender through appearance and mannerisms). Multivariable logistic regression models were used to analyze the associations among gender diversity measures and eating disorder symptoms, adjusting for potential confounders. RESULTS: Greater felt gender diversity was associated with self-worth tied to weight (OR 1.30, 95% CI 1.11-1.53), binge eating (OR 1.24, 95% CI 1.06-1.46), and distress with binge eating (OR 1.32, 95% CI 1.09-1.59). Greater gender expression diversity was associated with self-worth tied to weight (OR 1.16, 95% CI 1.02-1.33), distress with binge eating (OR 1.26, 95% CI 1.04-1.51), and characteristics of binge eating episodes (OR 1.33, 95% CI 1.06-1.66). Gender non-contentedness was associated with self-worth tied to weight (OR 1.38, 95% CI 1.20-1.58) and compensatory behaviors related to weight gain (OR 1.12, 95% CI 1.01-1.26). Transgender identity was not significantly associated with any eating disorder symptoms. DISCUSSION: We found that greater gender diversity across multiple dimensions was associated with various eating disorder symptoms, and that measures beyond binary gender identity may be important to assess gender diversity in early adolescence.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".