Surveying Canadian Pain Physicians’ Attitudes and Beliefs Regarding Medical Cannabis for Chronic Noncancer Pain: A Qualitative Study
Bibliographic record
Abstract
Jeremy Y Ng,1 Halton Quach,1 Mark R Phillips,1 Jason W Busse1– 4 1Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; 2The Michael G. DeGroote Centre for Medicinal Cannabis Research, McMaster University, Hamilton, Ontario, Canada; 3Department of Anesthesia, McMaster University, Hamilton, Ontario, Canada; 4The Chronic Pain Centre of Excellence for Canadian Veterans, Hamilton, Ontario, CanadaCorrespondence: Jason W Busse, Michael G. DeGroote Centre for Medicinal Cannabis Research, McMaster University, Hamilton, L8S 4K1, Canada, Tel +1-905-525-9140 x21731, Fax +1-905-523-1224, Email bussejw@mcmaster.caBackground: Medical cannabis is commonly and increasingly used by Canadians to manage chronic pain. As of March 2021, Health Canada reported that approximately 300,000 Canadians who were authorized to access medical cannabis, which is more than a 1000% increase from the 24,000 registered in 2015. Physicians, however, receive limited information on therapeutic cannabis during their training, and their perceptions regarding this therapeutic option are uncertain. This study focused on exploring attitudes and beliefs of pain physicians regarding medical cannabis for the management of chronic noncancer pain.Methods: This study utilized a focused ethnography approach. Pain management clinicians within the Greater Toronto and Hamilton Area were recruited through snowball sampling methods, and individually interviewed. We applied thematic analysis to interview transcripts and identified representative quotes. The Hamilton Integrated Research Ethics Board reviewed and approved this project.Results: Thirteen physicians who focused their clinical practice on pain management agreed to be interviewed, and three themes regarding medical cannabis emerged: 1) evidence regarding medical cannabis, 2) medical cannabis as first-line therapy for chronic pain, and 3) barriers to accessing medical cannabis. Subthemes of the last theme included out-of-pocket costs, stigma by society and healthcare providers, and lack of knowledge among physicians.Conclusion: Despite increasing use of medical cannabis for chronic pain among Canadians, pain physicians in our study expressed concerns regarding the evidence to support this therapy and acknowledged important barriers to access.Keywords: medical cannabis, chronic pain, qualitative research, focused ethnography, pain management
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".