Susceptibility to cognitive distortions: the role of eating pathology
Bibliographic record
Abstract
BACKGROUND: Thought-Shape Fusion (TSF) and Thought-Action Fusion (TAF) are cognitive distortions that are associated with eating and obsessional pathology respectively. Both involve the underlying belief that mere thoughts and mental images can lead to negative outcomes. TSF involves the belief that food-related thoughts lead to weight gain, body dissatisfaction, and perceptions of moral wrong-doing. TAF is more general, and involves the belief that merely thinking about a negative event (e.g., a loved one getting into a car accident) can make this event more likely to happen, and leads to perceptions of moral wrong-doing. However, the shared susceptibility across related cognitive distortions-TAF and TSF-has not yet been studied. METHOD: The effects of TSF and TAF inductions in women with an eating disorder (n = 21) and a group of healthy control women with no history of an eating disorder (n = 23) were measured. A repeated-measures design was employed, with all participants exposed to a TSF, TAF and neutral induction during three separate experimental sessions. Participants' cognitive and behavioral responses were assessed. RESULTS: Individuals with eating disorders were more susceptible to TSF and TAF than were control participants, demonstrating more neutralization behavior after TSF and TAF inductions (i.e., actions to try to reduce the negative effects of the induction), and reporting higher levels of trait TAF and TSF than did controls. CONCLUSIONS: Individuals with eating disorders are particularly susceptible to both TAF and TSF. Clinical implications of these findings will be discussed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".