An integrated substance use treatment model for young adults with first‐episode psychosis: A naturalistic pilot evaluation
Bibliographic record
Abstract
AIM: Approximately 50% of individuals with first-episode psychosis meet criteria for a substance use disorder and these concurrent disorders are associated with worse long-term outcomes. Psychosocial interventions, including motivational interviewing as well as cognitive and behavioural therapies, have shown some evidence for effective treatment in substance use disorders; however, there is a paucity of existing studies that have successfully examined these interventions in first-episode psychosis. METHODS: Participants (n = 64) received the concurrent disorders intervention, which included individual support alongside participation in at least one of two groups: a 4-week Motivational Engagement group utilizing motivational interviewing (n = 59) and an 8-week Relapse Prevention Training group emphasizing skill acquisition, which some participants entered directly (n = 5) and some participants entered following completion of the Motivational Engagement group (n = 16). RESULTS: Participants who completed the Motivational Engagement group (n = 59) demonstrated significantly increased motivation to change substance use (d = -.0.58; t = -3.02, p < .01) and significantly decreased substance use frequency (d = 0.65; t = 3.26, p < .01). For participants who completed the Relapse Prevention Training group (n = 21), substance use frequency significantly decreased (d = 0.92; t = 3.46, p < .01) and self-efficacy in one's ability to maintain substance use changes significantly increased (d = -0.85; t = -3.59, p < .01). CONCLUSIONS: This pilot evaluation suggests that motivational interviewing and relapse prevention skills training are acceptable and feasible interventions in the treatment of substance use disorders in young adults with first-episode psychosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".