Patterns, predictors and impact of substance use in early psychosis: a longitudinal study
Bibliographic record
Abstract
OBJECTIVE: The purpose was to determine the prevalence of substance use and its impact on outcome 3 years after presentation for a first-episode of psychosis. METHOD: Subjects were 203 consecutive admissions to an early psychosis program. Assessments included substance use, positive, negative and depressive symptoms and social functioning. Assessments occurred at baseline, and 1-, 2- and 3-year follow-ups. RESULTS: The prevalence of substance misuse was high with 51% having a substance use disorder (SUD), 33% with cannabis SUD and 35% with an alcohol SUD. Numbers with an alcohol SUD declined considerably by 1 year and for cannabis SUD by 2 years. Substance misuse was significantly associated with male gender, young age and age of onset and cannabis misuse with increased positive symptoms. CONCLUSION: This study confirms the high rates of substance misuse, in particular cannabis, in first-episode psychosis. It further demonstrates that these rates can be reduced.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".