Is it couture or a sickness: A narrative review on eating disorder behaviors in fashion models
Bibliographic record
Abstract
BackgroundThe modeling industry idealizes extremely low body mass, which may lead to the development of eating disorders (EDs) in models.AimsThis review examines the impact this has on model body habitus, disordered eating behaviors and ED diagnoses in models, and the mental health of fashion models.MethodsIn February 2023, search terms "fashion models" and "eating disorders" were used on PubMed, EBSCO, Embase, Scopus, Research Gate, Springer Access, Science Gate, and Google Scholar. Published peer-reviewed studies were included. Exclusion criteria included non-English articles, case studies, non-peer-reviewed articles, and non-relevant studies. Nineteen papers were selected and categorized into three subtopics: Physical characteristics of models, unhealthy weight control behaviors in modeling, and ED diagnoses in models.ResultsModels have significantly lower body mass index than controls and many engage in dysfunctional eating. There is mixed evidence on whether models have higher rates of EDs than non-models, though studies show a significantly higher rate of subclinical ED behaviors in models.ConclusionThere is likely an increased risk of subclinical disordered eating behaviors in models. Couture manufacturers need to reflect on how it can protect the health of the professionals who popularize their products.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".