Substance Use and Abuse in First-Episode Psychosis: Prevalence Before and After Early Intervention
Bibliographic record
Abstract
UNLABELLED: Despite the high prevalence of substance abuse among first-episode psychosis (FEP) populations, few studies examine whether early intervention (EI) improves substance abuse. OBJECTIVE: To examine the prevalence and pattern of substance use and abuse among an FEP sample over 12 months. METHODS: All the participants were diagnosed with a first episode of a schizophrenia spectrum disorder. The participants were followed prospectively. The prevalence rates of substance use and abuse from this sample were compared before and after 12 months of EI services and were compared with rates observed in a sample from the general population. RESULTS: A total of 200 participants (80.0% males; mean age 24 years) entered the study: 183 participants completed all the assessments at baseline, 131 participants completed all the assessments at 12 months. At baseline, the findings showed similar prevalence rates between the FEP sample and the general sample for lifetime cannabis use (60% vs 55%, respectively) and hazardous alcohol use (26% vs 21%) but significantly different prevalence rates for lifetime hallucinogen (29% vs 15%; P < .001) and cocaine use (20% vs 14%; P < .001). At 12 months, the prevalence rates for drug abuse (P < .01), hazardous alcohol use (P < .01), and concurrent drug abuse and hazardous alcohol use (P < .05) were significantly lower than at baseline. CONCLUSION: Substance use and abuse decreased significantly after 12 months of EI services; EI services may be able to detect and to reduce substance use among FEP patients before it becomes a more serious disorder.
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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 | 0.000 |
| 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) | 0.002 | 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".