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Record W3088404198 · doi:10.14740/jocmr4311

The Use of Acceptance and Commitment Therapy in Substance Use Disorders: A Review of Literature

2020· review· en· W3088404198 on OpenAlexvenueno aff
Joy Osaji, Chiedozie Ojimba, Saeed Ahmed

Bibliographic record

VenueJournal of Clinical Medicine Research · 2020
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAcceptance and commitment therapyMedicinePsycINFOAbstinencePsychological interventionSubstance useCINAHLPolysubstance dependenceDiscontinuationIntervention (counseling)ChecklistPsychiatrySubstance abusePsychotherapistMEDLINEPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Acceptance and commitment therapy (ACT) is a form of behavioral therapy that teaches people to learn to accept rather than avoid challenging situations in their lives. ACT has shown to be an intervention with great success in the reduction of various mental disorders and substance use disorders (SUDs). The core of ACT when used in SUD treatment is guiding people to accept the urges and symptoms associated with substance misuse (acceptance) and use psychological flexibility and value-based interventions to reduce those urges and the symptoms (commitment). The purpose of this study is to review the existing literature to examine the evidence on the use of ACT in the management of SUD. METHODS: A thorough search of four databases (CINAHL, PubMed.gov, PsycINFO and PsycNET) from 2011 to 2020 was conducted using search terms like ACT, ACT and SUD, ACT, and substance misuse. The articles retrieved were critically appraised using the Critically Appraised Topic (CAT) Checklist. RESULTS: Most of the studies showed that ACT was effective in the management of SUD showing significant evidence of a reduction in substance use or total discontinuation with subsequent abstinence. CONCLUSIONS: The literature review concluded that success has been achieved using ACT either as monotherapy or in combination with other therapy in the treatment of individuals with 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 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)0.010
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.640
GPT teacher head0.636
Teacher spread0.004 · 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 designOther design
Domainnot available
GenreReview

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

Citations68
Published2020
Admission routes1
Has abstractyes

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