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Record W4409012114 · doi:10.1016/j.eatbeh.2025.101973

Beyond thinness: The contribution of muscularity-oriented disordered eating to clinical impairment across cultures

2025· article· en· W4409012114 on OpenAlexaffabout
Lisa Y Zhu, Taylor N. Breddy, Reza N. Sahlan, Kerstin K. Blomquist, Lindsay P. Bodell

Bibliographic record

VenueEating Behaviors · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsWestern University
FundersFurman University
KeywordsPsychologyDisordered eatingDevelopmental psychologyEating disordersClinical psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.407
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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