Eating disorders among an online sample of Canadian and American boys and men
Bibliographic record
Abstract
There is a continued need to identify the prevalence and sociodemographic correlates of eating disorders, particularly among the under-researched group of boys and men, to inform prevention and intervention efforts. Data from The Study of Boys and Men, a sample of 1553 boys and men aged between 15 and 35 years in Canada and the United States, were analyzed in 2024. Probable eating disorder diagnoses were identified using a previously established algorithm based on current diagnostic criteria. A multivariable logistic regression analysis was used to determine the sociodemographic correlates of meeting the criteria for any probable eating disorder diagnosis. The prevalence of meeting the criteria for any probable eating disorder diagnosis was 21.3 % (95 % confidence interval [CI] 18.7-24.1), while meeting criteria for a probable bulimia nervosa diagnosis had the highest prevalence (5.8 %, 95 % CI 4.6-7.1) and anorexia nervosa had the lowest prevalence (0.34 %, 95 % CI 0.1-0.8). Boys and men who identified as gay (adjusted odds ratio [AOR] 2.28, 95 % CI 1.35-3.85) or bisexual (AOR 2.22, 95 % CI 1.23-3.99) had higher odds of meeting criteria for any probable eating disorder diagnosis, compared to those who did not. Finally, boys and men who had a higher body mass index (BMI) (AOR 1.18, 95 % CI 1.14-1.23) had greater odds of meeting criteria for any probable eating disorder diagnosis. Findings add to the growing understanding of eating disorders among boys and men. Targeted and tailored prevention and intervention programming is needed for sexual minority boys and men, and those with higher BMIs.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".