Emotion regulation facets prospectively distinguish food addiction from substance misuse in women with binge eating, gambling, or both behaviours
Bibliographic record
Abstract
OBJECTIVE: To determine the degree of similarity between food addiction (FA) and substance addictions (SA), we prospectively compared emotion regulation deficits associated with FA and alcohol or drug misuse in women with binge eating, gambling, or both behaviours. METHOD: Participants were 202 community-recruited women who engaged in at-risk binge eating (39 %), at-risk gambling (18 %), or both (43 %). Participants completed online assessments every two months for six months. The baseline and six-month surveys assessed self-reported emotion regulation using the Difficulties in Emotion Regulation Scale (DERS) and the UPPS-P Impulsivity Scale, FA using the Yale Food Addiction Scale, and alcohol and substance misuse using the Daily Drinking Questionnaire, Alcohol Use Disorders Identification Test, Drug Use Frequency, and Drug Abuse Screening Test. Two- and four-month surveys assessed only binge eating and gambling. RESULTS: We identified facets of emotion regulation that were cross-sectionally and longitudinally associated with FA and substance misuse. Negative urgency emerged as a common cross-sectional correlate of FA and substance misuse, whereas positive urgency and non-acceptance of one's negative emotions had different associations to FA versus substance misuse. Positive urgency prospectively predicted 100 %, 130 %, and 200 % increases in odds of future substance misuse problems and a 50 % decrease in odds of future FA, whereas being unaccepting of one's negative emotions was associated with more severe FA symptoms (a DERS subscale) and less severe alcohol-related problems. CONCLUSIONS: These findings suggest that FA is not associated with the same key deficits in emotion regulation as SA.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".