Emerging trends in eating disorders among sexual and gender minorities
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the recent literature on eating disorders, disordered eating behaviors (DEB), and body image dissatisfaction among sexual and gender minority populations, including, but not limited to, gay, lesbian, bisexual, and transgender people. RECENT FINDINGS: Overall, eating disorders, DEB, and body dissatisfaction are common among sexual and gender minority populations. Lifetime prevalence for anorexia nervosa (1.7%), bulimia nervosa (1.3%), and binge-eating disorder (2.2%) diagnoses are higher among sexual minority adults compared with cisgender heterosexual adults in the United States. Lifetime prevalence of eating disorders by self-report of a healthcare provider's diagnosis are 10.5% for transgender men and 8.1% for transgender women in the United States, including anorexia nervosa (4.2 and 4.1%) and bulimia nervosa (3.2 and 2.9%), respectively. DEB may be perpetuated by minority stress and discrimination experienced by these individuals. Body dissatisfaction may be a core stressor experienced by transgender people; gender dysphoria treatment has been shown to increase body satisfaction. A particular clinical challenge in caring for transgender youth with eating disorders is the standard use of growth charts based on sex. SUMMARY: Novel research demonstrates that sexual and gender minorities with eating disorders have unique concerns with regards to disordered eating and body image.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".