A group intervention which assists patients with dual diagnosis reduce their drug use: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: There is a well-recognized association between substance use and psychotic disorders, sometimes described as 'dual diagnosis'. The use of substances by people with psychosis has a negative impact in terms of symptoms, longitudinal course of illness and psychosocial adjustment. There are few validated treatments for such individuals, and those that do exist are usually impracticable in routine clinical settings. The present study employs a randomized controlled experimental design to examine the effectiveness of a manualized group-based intervention in helping patients with dual diagnosis reduce their substance use. METHOD: The active intervention consisted of weekly 90-min sessions over 6 weeks. The manualized intervention was tailored to participants' stage of change and motivations for drug use. The control condition was a single educational session. RESULTS: Sixty-three subjects participated, of whom 58 (92%) completed a 3-month follow-up assessment of psychopathology, medication and substance use. Significant reductions in favour of the treatment condition were observed for psychopathology, chlorpromazine equivalent dose of antipsychotics, alcohol and illicit substance use, severity of dependence and hospitalization. CONCLUSIONS: It is possible to reduce substance use in individuals with psychotic disorders, using a targeted group-based approach. This has important implications for clinicians who wish to improve the long-term outcome of their patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".