Change in emotion regulation during the course of treatment predicts binge abstinence in guided self-help dialectical behavior therapy for binge eating disorder
Bibliographic record
Abstract
BACKGROUND: Dialectical behavior therapy (DBT), which appears to be an effective treatment for binge eating disorder (BED), focuses on teaching emotion regulation skills. However, the role of improved emotion regulation in predicting treatment outcome in BED is uncertain. METHODS: This secondary analysis explored whether change in self-reported emotion regulation (as measured by the Difficulties in Emotion Regulation Scale) during treatment was associated with abstinence from binge eating at post-treatment and 4-, 5-, and 6-month follow-up in individuals who received a guided self-help adaptation of DBT for BED. Participants were 60 community-based men and women with BED who received a self-help manual and six 20-minute support phone calls. RESULTS: Greater improvement in self-reported emotion regulation between pre- and post-treatment predicted abstinence from binge eating at post-treatment, 4-, 5-, and 6-month follow-up. However, some follow-up results were no longer significant when imputed data was excluded, suggesting that the effect of emotion regulation on binge abstinence may be strongest at 4-month follow-up but decline across a longer duration of follow-up. CONCLUSIONS: This study provides preliminary support for the theoretical role played by improved emotion regulation in achieving binge eating abstinence. If this finding is replicated with larger samples, further research should identify specific techniques to help more individuals to effectively regulate their emotions over a longer duration.
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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 | 0.003 |
| 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 | 0.000 | 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".