Testing predictions of the emotion regulation model of binge‐eating disorder
Bibliographic record
Abstract
OBJECTIVE: The emotion regulation (ER) model of binge eating posits that individuals with binge-eating disorder (BED) experience more intense emotions and greater difficulties in ER than individuals without BED, leading them to binge eat as a means of regulating emotions. According to this model, individuals with BED should report greater difficulties in ER than their non-BED counterparts, the severity of these difficulties should be positively associated with BED symptoms, and this association should be stronger when individuals experience persistent negative emotions (i.e., depression). Studies examining these hypotheses, however, have been limited. METHOD: Data were collected from adults meeting the DSM 5 criteria for BED (n = 71; 93% female) and no history of an eating disorder (NED; n = 79; 83.5% female). Participants completed self-report measures of difficulties in ER, eating disorder (ED) psychopathology, and depression. RESULTS: Individuals with BED reported greater difficulties in ER compared to those with NED. Moreover, difficulties in ER predicted unique variance in binge frequency and ED psychopathology in BED. Depression moderated the association between ER difficulties and binge frequency such that emotion dysregulation and binge frequency were positively associated in those reporting high, but not low, depression levels. DISCUSSION: The association between difficulties in ER and ED pathology in BED suggests that treatments focusing on improving ER skills may be effective in treating this ED; however, the moderating effect of depression underscores the need for research on individual differences and treatment moderators. These findings suggest the importance of ER in understanding and treating BED.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".