Beyond thinness: The contribution of muscularity-oriented disordered eating to clinical impairment across cultures
Bibliographic record
Abstract
Muscularity-oriented disordered eating (MODE) has been linked to negative outcomes, even when controlling for thinness-oriented disordered eating; however, its contribution to clinical impairment across demographic groups remains understudied. This study examined whether MODE independently contributes to clinical impairment while accounting for cognitive restraint, restricting, and purging, and whether this relationship differs by gender and country. Female (n = 1575) and male (n = 906) students from Canada, the United States, and Iran completed self-report measures of MODE, thinness-oriented disordered eating, and clinical impairment. Hierarchical multiple regressions were conducted with gender and country as moderators. Analyses were pre-registered on Open Science Framework. The addition of MODE to the model significantly accounted for 11 % of unique variance in clinical impairment. No significant moderation effects of gender and country were found. Results suggest that across women and men from both Western and non-Western cultural contexts, the unique aspects of MODE are associated with disruptions in daily functioning in multiple domains. These findings challenge the belief that dieting for muscularity is inherently beneficial for well-being. More clinical attention on MODE is warranted, such as targeted prevention and treatment efforts that address MODE specifically, rather than conceptualizing it as an extension of thinness-oriented eating disorders.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".