The effect of alcohol and cannabis on quality of life and functioning in first episode psychosis patients
Bibliographic record
Abstract
chizophrenia is one of the leading causes of disability worldwide. New early intervention programs have shown some success in managing initial symptoms and improving the course of illness by beginning treatment during the first psychotic episode. Although these programs are a step forward towards recovery for people with schizophrenia and other psychotic disorders, in order for patients to attain a good quality of life, research needs to identify barriers that negatively interfere with symptom remission and illness management. Substance misuse has been identified in the literature as a prevalent issue within first episode psychosis patients and has been shown to negatively affect clinical outcomes; however, little is known about its impact on quality of life and functioning. This study aimed to address this gap in knowledge by examining the impact of course of substance misuse on quality of life, symptomatology, and global functioning over a 24-month period. A hundred and eighty-nine participants were categorized into one of three course of substance misuse groups: continued misuse, discontinued misuse and no misuse. No significant differences were observed between course of cannabis misuse groups (continued misuse, discontinued misuse or no misuse) over 24-months on any outcome variable; however, there was a significant course of alcohol misuse group difference on the positive symptoms outcome. It was found that those who continued misuse of alcohol had more positive symptoms than those who never misused alcohol or those who discontinued misuse of alcohol. These results indicate that alcohol misuse does impact clinical outcomes among first episode psychosis patients and that more research is needed to understand the full impact of substance misuse on this population.
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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 | 0.006 |
| 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 | 0.000 | 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".