Overlapping patterns of recreational and medical cannabis use in a large community sample of cannabis users
Bibliographic record
Abstract
BACKGROUND: Regulatory changes are increasing access to both medical cannabis and cannabis in general. As such, understanding patterns of recreational and medical cannabis use is a high public health priority. OBJECTIVES: Patterns of cannabis use (recreational and medical), other substance use, and psychiatric symptoms were characterized in a large sample of community adult cannabis users in Canada, prior to federal cannabis legalization. METHODS: This was a self-report assessment of 709 cannabis users (Mean age = 30.19 (11.82) years; 55.01% female). Patterns of overall substance use and psychiatric symptomatology were compared based on recreational/medical cannabis status. RESULTS: Overall, 61.4% of participants endorsed exclusively recreational use, while 38.6% reported some level of medical use. Of all medical users, only 23.4% reported authorization from a health professional. Recreational cannabis users typically reported infrequent use (less than weekly), whereas medical users modally reported daily use. Compared to recreational users, medical users reported more problematic cannabis use in addition to greater psychiatric symptomatology (anxiety, depression and trauma). Interestingly, a large majority of medical users also reported using recreationally (80.6%), while exclusive medical use was less common (19.3%). This dual motives group reported more daily cannabis use and more alcohol and tobacco use. Compared to medical-only users, individuals using cannabis for both medical and recreational purposes more often used cannabis to treat psychiatric conditions. CONCLUSIONS: These findings reveal the differences in cannabis use patterns and preferences between recreational and medical users, and even within medical users. In particular, dual motives individuals, who use cannabis for both positively and negatively reinforcing purposes, may warrant special attention as a subpopulation.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".