Associations between eating disorders and illicit drug use among college students
Bibliographic record
Abstract
OBJECTIVE: To estimate the associations between a positive eating disorder screen and any lifetime eating disorder diagnosis and illicit drug use among a large, diverse sample of college students. METHOD: We analyzed data from the national (United States), cross-sectional 2018-2019 Healthy Minds Study (HMS; n = 42,618; response rate: 16%). HMS collects information on the physical, mental, and social health of college students. Multiple logistic regression analyses were used to estimate the association between a positive eating disorder screen (measured using the SCOFF) and any self-reported lifetime eating disorder diagnosis and self-reported illicit drug use in the past 30 days (any illicit drug use and use of marijuana, cocaine, heroin, methamphetamines, stimulants, ecstasy, opioids, benzodiazepines), while adjusting for potential confounders. RESULTS: Among the sample, 54.34% (n = 28,608) were female and the mean age of participants was 23.30 (SE ± 0.05) years. Logistic regression analyses revealed unique associations between a positive eating disorder screen and any lifetime eating disorder diagnosis and illicit drug use among the sample of college student participants. A positive eating disorder screen was most strongly associated with methamphetamine use (adjusted odds ratio [AOR] 3.93, 95% confidence interval [CI] 1.43-10.78), and any lifetime eating disorder diagnosis was most strongly associated with benzodiazepine use (AOR 3.42, 95% CI 2.28-5.13). DISCUSSION: Illicit drug use is common among college students who screen positive for an eating disorder and report any lifetime eating disorder diagnosis. The co-occurring nature of eating disorders and illicit drug use may complicate treatment and lead to compounded adverse health outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".