Cannabis use patterns among patients with upper extremity conditions at the time of legalization in Canada
Bibliographic record
Abstract
BACKGROUND: Recreational cannabis use was legalized in Canada in 2018. Cannabis use patterns and patient attitudes toward cannabis use, particularly in the context of these legal changes, are not well understood. Our aim was to evaluate baseline cannabis use patterns and attitudes at the time of legalization among patients with upper extremity conditions in Canada. METHODS: In 2018, we conducted a multicentre cross-sectional survey study of 1561 patients with upper extremity conditions at 7 surgical centres. Participants were asked whether they currently use cannabis. If yes, they were asked questions regarding usage patterns and perceptions of cannabis use, including likelihood of use, safety and comfort discussing it with their physician. RESULTS: In the 6 months after legalization, 790 (51%) participants felt that cannabis was safer than prescription narcotics, with 450 (29%) currently using cannabis. Reasons for cannabis use included pain (56%), stress (51%) and recreation (42%). Of the 1105 patients not using cannabis, 267 (24%) were more likely to consider it after legalization. Of the 450 cannabis users, 73 (16%) had been using it for less than 6 months, 206 (46%) stated they were more comfortable discussing cannabis with their physician after legalization and 195 (43%) were using cannabis more than 4 times per week. CONCLUSION: Many patients with upper extremity conditions were regularly using cannabis. Patients were more comfortable discussing cannabis with their physician than before legalization. Treating surgeons should be aware of these trends and expect to receive questions regarding cannabis use.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".