Comparing a novel, virtual, group-based guided self-help to unguided self-help for the treatment of binge-eating disorder in adults: a randomized controlled trial
Bibliographic record
Abstract
Binge Focused Therapy (BFT) is a 3-session, group-based, guided self-help treatment for binge-eating disorder (BED). In this parallel-group randomized controlled trial (RCT), adults with BED were randomized to virtual BFT or a traditional unguided self-help approach (Overcoming Binge Eating; Fairburn, 2013). Self-report measures were collected at baseline, week 6, week 10 (posttreatment), 6- and 12-month follow-up. We hypothesized BFT (n = 82) would lead to better BED outcomes and lower dropout than unguided self-help (n = 82). Our intention-to-treat analysis demonstrated a significant effect of treatment group on BED symptomatology (primary outcome; β= − 5.04, p < .001, 95% CI [ − 7.57, − 2.52]), binge frequency (β= − 3.24, p = .001, 95% CI [ − 5.22, − 1.26]), general ED symptomatology (β= − 0.91, p < .001, 95% CI [ − 1.17, − 0.65]), clinical impairment (β= − 6.27, p < .001, 95% CI [ − 8.78, − 3.77]), confidence to change binge eating (β = 1.22, p < .001, 95% CI [0.56, 1.89]), BED remission (OR = 4.98, p = .003, 95% CI [1.72, 14.40]), and treatment attrition (β = 0.456, p < .001), with the BFT group reporting greater improvements and lower dropout. We did not find evidence of a significant effect of group on binge-eating abstinence (OR = 2.01, p = .103, 95% CI [0.87, 4.64]). BFT may be an effective BED treatment that could overcome common barriers to treatment implementation and accessibility.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".