Eating Disorder Risk and Diagnosis Among East Asian Youth in the United States: Findings From the Healthy Minds Study, 2020–2023
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of probable eating disorders and self-reported eating disorder diagnoses among East Asian young adults aged 18-25 years across US colleges. METHOD: Using data from the 2020 to 2023 Healthy Minds Study, a repeated cross-sectional survey of US college students, we analyzed data from East Asian and White participants aged 18-25 years (N = 160,740). Eating disorder risk was assessed using the SCOFF questionnaire and eating disorder diagnoses were self-reported. Using multivariable logistic models, we generated odds ratios (OR) and confidence intervals (CI) to estimate inequities in the prevalence of a probable eating disorder and eating disorder diagnoses between East Asian and White young adults, adjusting for gender identity, age, international student status, sexual orientation, financial stress, and study year. RESULTS: We found no statistically significant differences in the prevalence of a probable eating disorder among East Asian young adults compared to White young adults (OR: 1.04; 95% CI: 0.97-1.11) after adjustment. Among those with a probable eating disorder (n = 68,651), East Asian young adults had nearly half the odds (OR: 0.55; 95% CI: 0.47-0.65) of having a self-reported diagnosed eating disorder compared to White young adults. DISCUSSION: While the prevalence of having a probable eating disorder was similar among East Asian and White young adults, East Asians had almost half the odds of self-reporting an eating disorder diagnosis compared to White young adults. Future research is warranted to better understand barriers to eating disorder diagnosis among East Asian young adults in the US.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".