Over‐evaluation of thoughts about food: Differences across eating‐disorder subtypes and a preliminary examination of treatment effects
Bibliographic record
Abstract
ABSTRACT Objective Over‐evaluation of food, shape and weight is a multi‐faceted component of cognitive‐behavioral models of eating disorders. One specific aspect of over‐evaluation of food is a cognitive distortion known as thought‐shape fusion (TSF). TSF is purported to be specific to eating pathology; however, research has not yet elucidated whether individuals across the subtypes of eating disorders are differentially susceptible to this phenomenon. Furthermore, it remains unclear whether susceptibility to TSF decreases over the course of treatment. Method TSF, eating pathology, and generalized psychopathology were assessed in 76 individuals with eating disorders. Changes in TSF from pre‐ to post‐treatment were assessed in a subset of participants (n = 24). Results Individuals with the binge/purge subtype of anorexia nervosa were more susceptible to TSF than were individuals with bulimia nervosa or the restrictive subtype of anorexia nervosa. Increased TSF corresponded with higher levels of eating pathology, depression, and impulsivity. In addition, there were decreases in TSF over the course of treatment. Discussion The observed differences in TSF susceptibility across eating disorder subtypes suggests that subtypes may be differentially prone to over‐evaluation of thoughts about food, which represents a facet of one of the core maintenance mechanisms in cognitive‐behavioral models of eating disorders. © 2013 Wiley Periodicals, Inc. (Int J Eat Disord 2014; 47:302–309)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".