Prevalence of Cannabis Use among Youth and Young Adults Attending an Early Psychosis Intervention Program in Ontario, Canada
Bibliographic record
Abstract
Background: Cannabis use is associated with an increased risk of developing psychosis in youth and younger adults (YAYA). However, differences in accessibility and consumption patterns may influence prevalence rates in different populations. This study aimed to investigate the prevalence of cannabis use and its associations among young adults attending an Early Psychosis Intervention (EPI) program in Southeast Ontario. Methods: A cross-sectional study of 116 youth and young adults enrolled in the Southeast Ontario EPI program between 2016 and 2019. Demographic characteristics and psychiatric diagnoses were identified from the clinical records. Statistical analyses were performed to examine the prevalence of cannabis use, its correlation and association with alcohol and other substance use, and the association with psychotic disorders. Results: The study revealed a very high prevalence of cannabis use among YAYA attending the EPI program, with 82.8% self-reporting cannabis use. Males showed a higher prevalence of cannabis use (71.9% than females 28.1%), with a male-to-female ratio of 2.6:1. Significant associations were found between cannabis use and psychiatric disorders, including psychosis and substance-induced psychotic disorder (SIP) (P-value< 0.05). Conclusion: This study highlights the need for the screening and recognition of harmful cannabis use with specific and targeted interventions to reduce the potential serious mental health effects in youth and young adults presenting with early psychosis. Early intervention incorporating motivational enhancement, and lower-risk cannabis use alongside psychological and pharmacological therapies serve to reduce the harmful impact of cannabis, shortening the duration of untreated psychosis and supporting functional recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".