Group-Analytic and Psychoeducational Therapies for Binge-Eating Disorder: An Exploratory Study of Efficacy and Persistence of Effects
Bibliographic record
Abstract
Research has evaluated cognitive-behavioral therapy and interpersonal psychotherapy for the treatment of binge-eating disorder (BED); other therapies, however, have received less attention. The aim of our research was to analyze the efficacy of two group therapies for BED patients: analytic psychotherapy and psychoeducation. The psychotherapeutic intervention consisted of group-analytic psychotherapy of 14 sessions over a 28-week period; the group psychoeducational intervention involved 10 sessions over a 10-week period. The Eating Disorder Inventory-2, the 16-Personality Factors questionnaire, the Hospital Anxiety and Depression Scale, and the Toronto Alexithymia Scale-20 were used for psychometric assessment. Two follow-up assessments were performed after 6 and 12 months, respectively. At the end of treatment, most patients were without eating disorders and had a lower rate of binge episodes. The psychoeducational group patients improved markedly in alexithymic traits related to the ability to describe feelings. At follow-up, most patients were still without eating disorders and had few binge episodes. Although psychoeducational group patients confirmed the amelioration on alexithymic traits, analytic psychotherapy group patients showed a trend toward an improvement in personality traits related to the ability to be at ease when communicating with others.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".