Self‐compassion training for binge eating disorder: A pilot randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: The present pilot study sought to compare a compassion-focused therapy (CFT)-based self-help intervention for binge eating disorder (BED) to a behaviourally based intervention. DESIGN: Forty-one individuals with BED were randomly assigned to 3 weeks of food planning plus self-compassion exercises; food planning plus behavioural strategies; or a wait-list control condition. METHODS: Participants completed weekly measures of binge eating and self-compassion; pre- and post-intervention measures of eating disorder pathology and depressive symptoms; and a baseline measure assessing fear of self-compassion. RESULTS: Results showed that: (1) perceived credibility, expectancy, and compliance did not differ between the two interventions; (2) both interventions reduced weekly binge days more than the control condition; (3) the self-compassion intervention reduced global eating disorder pathology, eating concerns, and weight concerns more than the other conditions; (4) the self-compassion intervention increased self-compassion more than the other conditions; and (5) participants low in fear of self-compassion derived significantly more benefits from the self-compassion intervention than those high in fear of self-compassion. CONCLUSIONS: Findings offer preliminary support for the usefulness of CFT-based interventions for BED sufferers. Results also suggest that for individuals to benefit from self-compassion training, assessing and lowering fear of self-compassion will be crucial. PRACTITIONER POINTS: Individuals with BED perceive self-compassion training self-help interventions, derived from CFT, to be as credible and as likely to help as behaviourally based interventions. The cultivation of self-compassion may be an effective approach for reducing binge eating, and eating, and weight concerns in individuals with BED. Teaching individuals with BED CFT-based self-help exercises may increase their self-compassion levels over a short period of time. It may be important for clinicians to assess and target clients' fear of self-compassion for clients to benefit from self-compassion training interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| 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".