Prevalence and correlates of muscle dysmorphia in a sample of boys and men in Canada and the United States
Bibliographic record
Abstract
BACKGROUND: Muscle dysmorphia is a significant mental health condition that has been under-researched in epidemiological, community-based studies. Therefore, this study aimed to identify the prevalence and correlates of probable muscle dysmorphia among a sample of Canadian (n = 784) and American (n = 563) boys and men ages 15-35 years. METHODS: The sample comprised 1,488 boys and men who completed a variety of measures and items to capture sociodemographic characteristics and muscle dysmorphia symptoms. Diagnostic criteria were applied to identify probable muscle dysmorphia among the sample. Unadjusted (e.g., chi-square tests, independent samples t-tests) and adjusted (e.g., logistic regression) analyses were used to determine the sociodemographic factors (e.g., age, body mass index, gender, race/ethnicity, sexual orientation, education, relationship status, and country) associated with cases of probable muscle dysmorphia. RESULTS: The prevalence of probable muscle dysmorphia was 2.8% (95% confidence interval 2.0-3.7%). Aside from lower body mass index among those with probable muscle dysmorphia, there were no significant demographic differences between those with and without probable muscle dysmorphia across ages, genders, races/ethnicities, and sexual orientations. Those with probable muscle dysmorphia had significantly higher scores on standardized measures of muscle dysmorphia symptomatology and muscularity-oriented attitudes and behaviors compared to those without probable muscle dysmorphia. CONCLUSIONS: Findings underscore that muscle dysmorphia may be more prevalent among boys and men in Canada and the United States than previously thought, highlighting the need for more research, prevention, assessment, and intervention efforts. The minimal differences across sociodemographic factors are notable, highlighting the need for an inclusive understanding of muscle dysmorphia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".