[First Episode Psychosis and Substance Use Disorder: Narrative Review of Best Practices and Adapted Approaches for Assessment and Monitoring].
Bibliographic record
Abstract
Objectives About half of young adults with early psychosis also have substance use disorders (SUD). For young adults with first episode psychosis (FEP), the persistence of SUD negatively impacts the symptomatic and functional outcome as well as the management of the problems. This article aims to identify and synthetize the best therapeutic approaches for the treatment of young adults with concurrent disorders (FEP and SUD) and to present avenues for practical and adapted approaches for the assessment and follow-up of people with concurrent FEP and SUD. Method Narrative literature review on the treatment of young adults with concurrent disorder (FEP and SUD). Results Several studies demonstrate the usefulness of early intervention for psychosis services (EIS) in the management of SUD with approximately 50% decrease in SUD during the first year of follow-up. A variety of therapeutic interventions have been studied, but none have demonstrated substantial long-term superiority over the standard treatment offered in EIS. The studies also have several methodological limitations. To date, clinical guidelines suggest offering an adapted and integrated treatment for psychosis and SUD and recommend the use of various approaches such as case management, comprehensive assessment and feedback on SUD and psychosis as well as their interplay, harm reduction interventions, motivational interviewing, cognitive behavioural therapy, and pharmacotherapy. It is proposed here to proactively adjust the treatment to consider the severity of the disorders, their impact on the various dimensions of psychosis outcomes, the developmental stage of the young person and his or her stage of change with respect to substance use. Conclusion Data on best practice in the treatment of concurrent PEP-SUD disorders are relatively limited. Some approaches appear to have the potential to improve the clinical course of young people living with such conditions, especially if they are adapted to this population. Furthermore, research and innovations in the management of concurrent disorders must continue to offer better adapted care to young adults with early psychosis and SUD.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".