Testing a stepped care model for binge-eating disorder: a two-step randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: A stepped care approach involves patients first receiving low-intensity treatment followed by higher intensity treatment. This two-step randomized controlled trial investigated the efficacy of a sequential stepped care approach for the psychological treatment of binge-eating disorder (BED). METHODS: In the first step, all participants with BED (n = 135) received unguided self-help (USH) based on a cognitive-behavioral therapy model. In the second step, participants who remained in the trial were randomized either to 16 weeks of group psychodynamic-interpersonal psychotherapy (GPIP) (n = 39) or to a no-treatment control condition (n = 46). Outcomes were assessed for USH in step 1, and then for step 2 up to 6-months post-treatment using multilevel regression slope discontinuity models. RESULTS: In the first step, USH resulted in large and statistically significant reductions in the frequency of binge eating. Statistically significant moderate to large reductions in eating disorder cognitions were also noted. In the second step, there was no difference in change in frequency of binge eating between GPIP and the control condition. Compared with controls, GPIP resulted in significant and large improvement in attachment avoidance and interpersonal problems. CONCLUSIONS: The findings indicated that a second step of a stepped care approach did not significantly reduce binge-eating symptoms beyond the effects of USH alone. The study provided some evidence for the second step potentially to reduce factors known to maintain binge eating in the long run, such as attachment avoidance and interpersonal problems.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".