A description of self-medication with cannabis among adults with legal access to cannabis in Quebec, Canada
Bibliographic record
Abstract
Abstract Objective Cannabis is increasingly used for medical purposes, particularly in countries like Canada where cannabis was recently legalized for recreational use. We aimed to assess self-medication with cannabis post-cannabis legalization among adults in the Canadian province of Quebec. Methods This is a cross-sectional online survey of a self-selected convenience sample conducted in Quebec, Canada, from November 2020 to January 2021. Individuals aged ≥ 21 years who endorsed using cannabis bought in legal recreational cannabis stores to self-medicate a health condition were included. Data were analyzed using descriptive statistics and stratified according to sex, age, and the type of cannabis use (exclusively medical versus medical and recreational use). Results Four hundred eighty-nine participants were included. The median age was 34 years, and 48% were women. About 25% reported exclusive medical use of cannabis. Treated conditions included anxiety (70%), insomnia (56%), pain (53%), depression (37%), and many others. Reasons for not consulting in cannabis clinics included lack of information (52%), the complexity of the process (39%), accessibility of cannabis clinics (23%), and others. Tetrahydrocannabinol (THC) dosage > 20% was reported by 32%. Smoking was the main route of use (81%). Possession of prescribed drugs was reported by 56%. Professionals consulted for information on cannabis included recreational cannabis store agents (36%), physicians (29%), and others. Overall, significant differences were observed for many of the comparisons according to sex, age, and the type of cannabis use. Conclusions Many conditions are self-medicated with cannabis. The use of high doses of cannabis, smoking as a preferred method of use, and concurrent use of other medications may pose some risks to individuals. Addressing the reported barriers to medical access to cannabis is urgently needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.118 | 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 teacher head, 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".