The Use of Acceptance and Commitment Therapy in Substance Use Disorders: A Review of Literature
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".