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Record W2087282820 · doi:10.1080/10640266.2011.533603

Mindfulness-Action Based Cognitive Behavioral Therapy for Concurrent Binge Eating Disorder and Substance Use Disorders

2010· article· en· W2087282820 on OpenAlexaff
Christine Courbasson, Yasunori Nishikawa, Leah B. Shapira

Bibliographic record

VenueEating Disorders · 2010
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity Health NetworkCentre for Addiction and Mental HealthYork UniversityUniversity of Toronto
Fundersnot available
KeywordsMindfulnessBinge-eating disorderClinical psychologyBinge eatingEating disordersMindfulness-based cognitive therapyAddictionPsychiatryAlcohol use disorderPsychologyCognitive behavioral therapySubstance abusePopulationSubstance useCognitionCognitive therapyDepression (economics)Bulimia nervosaMedicineAlcohol

Abstract

fetched live from OpenAlex

Individuals with Binge Eating Disorder (BED) often evidence comorbid Substance Use Disorders (SUD), resulting in poor outcome. This study is the first to examine treatment outcome for this concurrent disordered population. In this pilot study, 38 individuals diagnosed with BED and SUD participated in a 16-week group Mindfulness-Action Based Cognitive Behavioral Therapy (MACBT). Participants significantly improved on measures of objective binge eating episodes; disordered eating attitudes; alcohol and drug addiction severity; and depression. Taken together, MACBT appears to hold promise in treating individuals with co-existing BED-SUD.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.364
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations77
Published2010
Admission routes1
Has abstractyes

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