Canadian Schizophrenia Guidelines: Schizophrenia and Other Psychotic Disorders with Coexisting Substance Use Disorders
Bibliographic record
Abstract
OBJECTIVE: Persons with schizophrenia and other psychotic disorders frequently have coexisting substance use disorders that require modifications to treatment approaches for best outcomes. The objectives of this review were to identify evidence-based practices best practices that improve outcomes for individuals with schizophrenia and substance used disorders. METHOD: We reviewed guidelines that were published in the last 5 years and that included systematic reviews or meta-analyses. Most of our recommendations came from 2 publications from the National Institute for Health and Care Excellence (NICE): the 2011 guidance titled Coexisting Severe Mental Illness (Psychosis) and Substance Misuse: Assessment and Management in Healthcare Settings and the 2014 guidance titled Psychosis and Schizophrenia in Adults: Prevention and Management. We placed these recommendations into the Canadian context to create this guideline. RESULTS: Evidence supports the inclusion of individuals with coexisting substance use disorders in first-episode psychosis programs. The programs should integrate psychosis and substance use treatments, emphasizing ongoing monitoring of both substance use and patterns and symptoms. The best outcomes are achieved with combined use of antipsychotic medications and addiction-based psychosocial interventions. However, limited evidence is available to recommend using one antipsychotic medication over another or one psychosocial intervention over another for persons with schizophrenia and other psychotic disorders with coexisting substance use disorders. CONCLUSIONS: Treating persons who have schizophrenia and other psychotic disorders with coexisting substance use disorders can present clinical challenges, but modifications in practice can help engage and retain people in treatment, where significant improvements over time can be expected.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".