Predictors of Cannabis Use Among Canadian University Students
Bibliographic record
Abstract
Objective: To examine the correlates between cannabis use, motives to use, related psychosocial outcomes and academic behaviours among a sample of Canadian university students. Methods: A random sample of 6,000 students who were enrolled in at least one class and were 18 years or over were asked to complete a cross-sectional online survey. Of the 920 students that responded, 478 (ages 18-55; mean age = 25.02, SD = 5.95) identified as having used cannabis within the past six months and thus were included as participants in the current study. Participants completed a battery of measures designed to examine cannabis use and associated constructs (i.e., substance use risk, personal well-being, non-specific psychological distress, academic behaviours, and motivations for use). Results: Among the participants, 31% (n = 148) were found to be frequent (i.e., hazardous) users. Using cannabis for enhancement, coping, expansion, sleep difficulties, and conformity purposes, as well as impulsive personality traits were found to be predictors of cannabis use severity, with the enhancement motive identified as the strongest predictor for the total sample, males, and hazardous users. The coping motive was the strongest predictor for females, and impulsivity was the strongest predictor for non-hazardous users. Conclusion: Findings will help inform the development of campus guidelines for lower risk cannabis use. Information gleaned from this study will also provide important information for those that use cannabis, policymakers, and health care providers in considering optimal personal use, prevention, and intervention plans.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".