Dialectical behavior therapy guided self-help for binge-eating disorder
Bibliographic record
Abstract
Binge-eating disorder (BED) is a prevalent and serious public health issue. BED is characterized by recurrent out-of-control binge eating episodes in the absence of extreme weight control behavior and is associated with significant psychosocial and physiological impairment. Dialectical Behavior Therapy (DBT), based on the affect regulation model of binge eating, is an evidence-based treatment (EBT) approach for BED. Unfortunately, access to EBTs is often limited due to geographical barriers (i.e., lack of local providers with specialized training in EBTs), lack of financial resources, and/or time constraints. Self-help approaches (via guided and unguided versions) to delivering DBT for BED offer a potentially effective means of more widely disseminating this treatment. Compared to traditional, higher intensity approaches, self-help DBT for BED is less time-consuming, less financially costly, and requires less need for specialized therapist-training. This paper will present how DBT for BED has been adapted for self-help delivery, review the limited but promising research on DBT self-help available to date, and provide directions for future research.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".