Cannabis Use for Medicinal Purposes among Canadian University Students
Bibliographic record
Abstract
Objective:The study sought to describe a Canadian sample of university students’ medicinal use of cannabis, including prevalence of cannabis use disorder (CUD) and replacement of traditional treatments with cannabis.Method:A random sample of 4000 university students was asked to complete a cross-sectional web-based survey. The survey was completed by 2212 (average age 23.2 years, SD = 5.2 years), representing a 55.3% response rate. To be eligible, students had to be enrolled in a class on campus and were 18 years or older.Result:Half (52%) of respondents used cannabis at least once in their lifetime, with ∼11% reporting medicinal cannabis use. Recreational motives to use cannabis were common among medicinal users (85%), several (38%) replaced traditional medication with cannabis, and more than a third received authorization by a health care provider. Of the medicinal users, 13.6% met the criteria for CUD. Common ailments for medicinal cannabis use were anxiety, sleep problems, depression, and pain. When mental health–related categories are combined, 78.2% of medicinal users used for at least 1 mental health condition.Conclusions:Medicinal cannabis use occurs among university students. None of the ailments listed by medicinal users meet the Canadian Family Physicians prescribing guidelines, and most are not among those viewed by the National Academies of Science, Engineering, and Medicine as having strong evidence for therapeutic value. The results raise concerns for health care providers who are authorizing or counselling patients’ considering medicinal cannabis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 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".