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Record W2980702188 · doi:10.1080/10640266.2019.1678982

Dialectical behavior therapy guided self-help for binge-eating disorder

2019· article· en· W2980702188 on OpenAlexaff
Therese E. Kenny, Jacqueline C. Carter, Debra L. Safer

Bibliographic record

VenueEating Disorders · 2019
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMemorial University of NewfoundlandUniversity of Guelph
Fundersnot available
KeywordsDialectical behavior therapyBinge-eating disorderBinge eatingPsychosocialPsychotherapistBulimia nervosaPsychologyClinical psychologyEating disordersPsychiatryBorderline personality disorder

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.026
GPT teacher head0.338
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2019
Admission routes1
Has abstractyes

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