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Record W4413981985 · doi:10.1080/15504263.2025.2515015

Concurrent Disorders and Treatment Outcomes: A Meta-Analysis

2025· review· en· W4413981985 on OpenAlexaff
Kevin M. Gorey

Bibliographic record

VenueJournal of Dual Diagnosis · 2025
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDisadvantagedPovertyPsychological interventionMeta-analysisMental healthPsychologyClinical psychologyPsychiatrySubstance abuseSubstance usePsychotherapistMedicinePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: This rapid review and meta-analysis explores two hypotheses. First, people with a concurrent mental health and substance use disorder (SUD) respond less favorably to currently utilized treatment interventions, than do those with a single disorder. Second, the potential for certain already vulnerable groups including women, members of racialized minority groups and those who live in or near poverty may be even further disadvantaged. METHODS: A multimethod sampling frame of 35 previous systematic reviews and or meta-analyses (2000-2024) augmented with peer-reviewed and grey research literature databases (2020-2024), resulted in the selection of 13 primary studies. RESULTS: The pooled, sample-weighted risk ratio of 1.71 (95% confidence interval 1.38, 2.13) seemed to strongly suggest that those with concurrent disorders are largely disadvantaged in treatment compared to those with a single disorder. CONCLUSIONS: The results of this review confirmed people with a concurrent disorder are twice as likely to experience such undesirable outcomes as relapse and related poor outcomes including emergency department visits, rehospitalization and death. However, no evidence was found enabling exploration of potential moderations of overall treatment effects by gender, race or income.

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 categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.011
Bibliometrics0.0010.001
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 head0.167
GPT teacher head0.423
Teacher spread0.256 · 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 designMeta-analysis
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

Citations9
Published2025
Admission routes1
Has abstractyes

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