Gender differences in the association between body shame and binge eating: Body image flexibility as a moderator in women
Bibliographic record
Abstract
BACKGROUND: Body shame is an established antecedent and maintaining factor of binge eating, beyond global negative affect and body weight, and partly through the effects of dietary restriction. These associations have mainly been established in women and are understudied in men. Body image flexibility, a concept from Acceptance and Commitment Therapy, may be a protective psychological process against the effects of negative body image on binge eating. METHODS: Using structural equation modeling and a non-clinical sample of 244 women and 185 men, this cross-sectional study tested an integrated model of the association between body shame and binge eating, mediated by dietary restriction and moderated by body image flexibility, controlling for depressive symptoms and BMI. RESULTS: Body shame predicted binge eating in women but not in men, whereas depressive symptoms predicted binge eating in men but not in women. Body image flexibility moderated the association between body shame and binge eating in women. CONCLUSIONS: These cross-sectional findings highlight the importance of considering gender differences in current negative affect models of binge eating, and further support the role of body image flexibility in attenuating the influence of body shame on binge eating in women. Longitudinal studies are required to examine the temporal dynamics between these variables.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".