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Record W4295897846 · doi:10.1037/adb0000883

Review of third-wave therapies for substance use disorders in people of color and collectivist cultures: Current evidence and future directions.

2022· review· en· W4295897846 on OpenAlexaff
Gil Angela Dela Cruz, Samantha Johnstone, Hyoun S. Kim, David Castle

Bibliographic record

VenuePsychology of Addictive Behaviors · 2022
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsToronto Metropolitan UniversityCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsycINFOMindfulnessPsychologyMEDLINEClinical psychologyAddictionSubstance abuseCollectivismMindfulness-based cognitive therapyPsychiatryCognitive therapyPsychotherapistMedicineCognition

Abstract

fetched live from OpenAlex

OBJECTIVE: In people of color (POC) and from collectivist cultures, third-wave therapies utilizing mindfulness may be a more sensitive approach to substance use disorder (SUD) treatment, than cognitive behavioral therapy (CBT). This systematic review examined this hypothesis. METHOD: We searched PsycINFO, Pubmed, and MEDLINE on December 23, 2021. Articles were included if they compared efficacy of third-wave therapies to therapies with only CBT elements and reported treatment outcomes for POC/people from collectivist cultures. RESULTS: = 2) to measure substance use. Overall, eight studies reported greater improvements in the third-wave therapy group relative to the CBT group in POC, on at least one substance use outcome. CONCLUSIONS: Findings suggest that relative to CBT, third-wave therapies are a promising modality in the treatment SUDs in POC and people and from collectivist cultures. However, studies are relatively sparse and carry a number of methodological problems. As such, there remains a need for further research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.115
GPT teacher head0.441
Teacher spread0.326 · 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 designSystematic review
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

Citations15
Published2022
Admission routes1
Has abstractyes

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