Experiences and Perceptions of Medical Cannabis among People Living with Chronic Pain and Community Pharmacists: A Qualitative Study in Canada
Bibliographic record
Abstract
Background: The use of cannabis to treat chronic pain is under debate despite high expectations from patients. Qualitative data obtained by exploring both patients' and health professionals' perspectives are scarce. Aims: This study aimed to understand the experiences and perceptions of people living with chronic pain and community pharmacists regarding the role of cannabis in chronic pain treatment in the Canadian context where both medical and recreational cannabis are legal. Methods: We conducted 12 online focus groups (July 2020-February 2021) with 26 patients and 19 community pharmacists using semistructured discussion guides. All discussions were audio recorded and transcribed verbatim were analyzed using a reflexive thematic approach. Results: We developed three themes related to patients' perspectives and three themes related to pharmacists' perspectives. Patients' perspectives included (1) cannabis as an alternative to other pain medications, (2) a new treatment with potential health-related risks, and (3) a therapy rather than a recreational drug. Pharmacists' perspectives included (1) challenges in monitoring drug interactions with cannabis in the context of scarce research data, (2) informing and treating patients self-medicating with cannabis amid its growing popularity, and (3) financial costs and legal constraints for patients. Conclusions: This study highlights patients' and pharmacists' urgent need for reliable information regarding the benefits and risks of cannabis. Training tailored to pharmacists' needs and evidence-based information for patients should be developed to support pharmacists' practice, improve patients' experiences, and promote safe cannabis use.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".