‘Feeling fat,’ eating pathology, and eating pathology-related impairment in young men and women
Bibliographic record
Abstract
‘Feeling fat’ has received little empirical attention despite clinical recognition as an eating disorder maintenance factor. This experience also occurs in non-clinical populations and may relate to elements of subclinical eating pathology. The present study examined whether ‘feeling fat’ independently contributes to eating pathology and eating pathology-related impairment, over and above over-evaluation of weight and shape and dysphoria. University students (N = 990; 54.3% female) completed questionnaires measuring these constructs. Moderated multiple hierarchical regression analyses evaluated ‘feeling fat’'s unique contribution to eating pathology and impairment, and the moderating effects of gender and eating disorder symptom severity. ‘Feeling fat’ accounted for significant unique variance in eating pathology, but not eating pathology-related impairment, over and above over-evaluation of weight and shape and dysphoria. The relationship between ‘feeling fat’ and eating pathology-related impairment was stronger in women than in men. Symptom severity did not moderate the relationship between ‘feeling fat’ and either dependent variable. ‘Feeling fat’ distinctly relates to eating pathology in a sample of young adults, suggesting that ‘feeling fat’ deserves attention in individuals without eating disorders. Future research should longitudinally investigate the direction of the relationship between ‘feeling fat’ and eating pathology and examine mechanisms of gender differences in ‘feeling fat.’
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".