T55. ATTITUDE AND PATTERNS OF CANNABIS USE IN A CLINICAL SAMPLE OF PATIENTS WITH SCHIZOPHRENIA
Bibliographic record
Abstract
Cannabis is the most commonly used illicit substance globally and the preferred substance of use among patients with schizophrenia. Cannabis use is not only associated with an increased risk of development of psychosis but also has detrimental effects on patients with schizophrenia. We conducted a survey on patterns of use among outpatients of Schizophrenia program at the Royal Ottawa Mental Health Center. This was a phase I of a larger study and aimed to assess patterns of Cannabis use prior to the legalization in Canada. Research volunteers approached 511 patients in the waiting room and invited them to take the following two surveys: 1) a 33-item investigator-generated questionnaire on details of cannabis use and attitude toward it, and 2) The Cannabis Use Disorder Identification test revised (CUDIT-r) questionnaire. Near 40% of the patients approached by research staff, agreed to participate in the study. Of the total 204 participants, 69% were male and mean age was 44.6 years. Life time and current cannabis use was reported as 53.5% and 25.9%, respectively. 45% of current users reported frequency of use of once per week and more. Cannabis users on average spent 157$ per month on cannabis. 25% of cannabis users felt cannabis has positive effects on their mental health, and 13% reported no effect. 41% reported positive or no effect on their function. 10% felt cannabis legalization may increase their use. The majority of participants (80%) was not interested in changing their cannabis use. This study describes the pattern of cannabis use and attitude toward it in a Canadian sample of patients with schizophrenia. Patients with schizophrenia have higher rate of and morbidity from cannabis use. Perceived stigma around cannabis use is likely contributing to the low rate of survey participation. The authors will further discuss the implications of these findings in clinical setting.
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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 | 0.000 | 0.002 |
| 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) | 0.003 | 0.001 |
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".