Perceived Risk of Medical Cannabis and Prescribed Cannabinoids for Chronic Pain: A Cross-Sectional Study Among Quebec Clinicians
Bibliographic record
Abstract
Objective: An increase in medical cannabis and prescribed cannabinoids use for chronic pain management has been observed in Canada in the past years. This study aimed to: 1) Describe clinicians’ perceived risk associated with the use of medical cannabis and prescribed cannabinoids for the management of chronic pain; and 2) Identify sociodemographic and professional factors associated with perceived risk of adverse effects. Method: A web-based cross-sectional study was conducted in Quebec, Canada in 2022. A convenience sample of 207 clinicians was recruited (physicians/pharmacists/nurse practitioners). They were asked to rate the risk of adverse effects associated with medical cannabis (e.g., smoke, or oil) and prescribed cannabinoids (e.g., nabilone) on a scale of 0 to 10 (0: no risk, 10: very high risk), respectively. Multiple linear regression was performed to identify factors associated with perceived risk. Results: Average perceived risk associated with medical cannabis and prescribed cannabinoids were 5.93 ± 2.08 (median:6/10) and 5.76 ± 1.81 (median:6/10). Factors associated with higher medical cannabis perceived risk were working in primary care (β = 1.38, p = .0034) or in another care setting (β = 1.21, p = .0368) as compared to a hospital setting. As for prescribed cannabinoids, being a pharmacist (β = 1.14, p = .0452), working in a primary care setting (β = 0.83, p = .0408) and reporting more continuing education about chronic pain (β = 0.02, p = .0416) were associated with higher perceived risk. No sex differences were found in terms of perceived risk. Conclusions: Considering the clinician’s experience provide insights on cannabis risk as these professionals are at the forefront of patient care when they encounter adverse effects.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".