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Record W4212834887 · doi:10.1017/s1352465822000042

Mechanisms and moderators of behavioural couples therapy for alcohol and substance use disorders: an updated review of the literature

2022· article· en· W4212834887 on OpenAlexaff
Christina Mutschler, Bailee L. Malivoire, Jeremiah A. Schumm, Candice M. Monson

Bibliographic record

VenueBehavioural and Cognitive Psychotherapy · 2022
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsToronto Metropolitan University
FundersBritish Association for Behavioural and Cognitive Psychotherapies
KeywordsPsychologyPsychological interventionMechanism (biology)PsychotherapistClinical psychologyDual diagnosisSubstance useRelapse preventionPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Behavioural couples therapy (BCT) and alcohol behavioural couples therapy (ABCT) are couples-based interventions for substance use disorders (SUDs) that have been deemed a 'gold standard' treatment. Despite the substantial amount of promising research, there is a lack of research on the active components of treatment and treatment mechanisms and moderators. Since the most recent meta-analysis, a number of studies have been conducted that advance our understanding of the efficacy of BCT and ABCT. AIMS: The purpose of the present review was to provide an update on the current knowledge of these treatments and to investigate mediators and moderators of treatment. METHOD: A systematic search strategy of relevant databases from 2008 to 2021 identified 20 relevant articles that were coded for relevant information including study design, treatment, outcomes, as well as mechanisms and moderators. RESULTS: The results indicated that BCT and ABCT are successful in reducing alcohol and substance use for both male and female clients, dual problem couples, and for reducing post-traumatic stress symptoms and intimate partner violence. The reviewed studies discussed a number of treatment mechanisms, with the most studied mechanism being relationship functioning. Moderators included relationship functioning and patient gender. CONCLUSIONS: The results point to the need for additional research on active treatment components, mechanisms and moderators, in order to provide a more efficient and cost-effective treatment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.060
GPT teacher head0.366
Teacher spread0.306 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2022
Admission routes1
Has abstractyes

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