Bibliographic record
Abstract
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 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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".