Body checking, body avoidance, and the core cognitive psychopathology of eating disorders: is there a unique relationship?
Bibliographic record
Abstract
The purpose of this study was to demonstrate a unique relationship between body checking and avoidance, and the overvaluation of weight/shape in eating disorders (EDs). We sought evidence for the theoretical premise that these behaviours are manifestations of the core cognitive psychopathology of EDs, and support for the inclusion of interventions targeting body checking and avoidance in ED treatment as a means to reducing overvaluation of weight/shape. Three hundred and seventy-one treatment-seeking individuals with EDs completed measures of depression, body dissatisfaction, self-esteem, body checking, body avoidance, and overvaluation of weight/shape at pre- and post-day hospital treatment. Body mass index was also calculated. Hierarchical regression revealed that overvaluation of weight/shape predicted body checking and body avoidance over and above the other variables at pretreatment, and changes in body checking and avoidance over the course of treatment predicted changes in overvaluation of weight/shape over and above changes in the other variables. However, the results were stronger for body checking than body avoidance. Overall, the results of this research are consistent with the transdiagnostic model of the maintenance of EDs and lend support to the use of interventions targeting body checking in particular, as a means to targeting the core psychopathology of the disorder.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".