Impact of tobacco, alcohol and cannabis use on treatment outcomes among patients experiencing first episode psychosis: Data from the national RAISE‐ETP study
Bibliographic record
Abstract
AIM: The primary aim of this study was to examine the effect of recent tobacco, alcohol and cannabis use on treatment outcomes among participants experiencing first episode psychosis (FEP). METHODS: Secondary data analyses were conducted on 404 participants enrolled in the Recovery After an Initial Schizophrenia Episode-Early Treatment Program (RAISE-ETP) study. RAISE-ETP investigated the effectiveness of a coordinated specialty care (CSC) intervention for FEP in community mental health agencies in the United States. Generalized estimating equations were used to examine whether recent tobacco smoking, alcohol, and cannabis use at baseline were associated with illness severity, number of antipsychotic pills missed, psychiatric symptoms and quality of life during the 24-month treatment period, after controlling for duration of untreated psychosis and treatment group. RESULTS: At baseline, roughly 50% (n = 209) of participants reported recent tobacco, 28% (n = 113) alcohol and 24% (n = 95) cannabis use. Tobacco smokers had higher levels of illness severity (β = .24; P < .005), a higher number of missed pills (β = 2.89; P < .05), higher psychiatric symptoms and lower quality of life during treatment relative to non-smokers. Alcohol users had a higher number of missed pills (β = 3.16; P < .05) during treatment and cannabis users had higher levels of illness severity (β = .18; P < .05) and positive symptoms (β = 1.56; P < .05) relative to non-users. CONCLUSIONS: Tobacco, alcohol and cannabis use are common in youth seeking treatment for FEP. Tobacco smoking was associated with more negative clinical outcomes. These findings have implications for including interventions targeting these areas of substance use within current CSC models.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".