Individuals’ Values and Preferences Regarding Medical Cannabis for Chronic Pain: A Descriptive Qualitative Study
Bibliographic record
Abstract
Hanson Cummings,1,* Moizza Zia Ul Haq,1,* Amne Dargham,1 Nauman Shakeel,1 Jason W Busse,1â 3 Andrea J Darzi,1,3 Elizabeth Alvarez1,4 1Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; 2Michael G DeGroote Centre for Medicinal Cannabis Research, McMaster University, Hamilton, Ontario, Canada; 3Department of Anesthesia, McMaster University, Hamilton, Ontario, Canada; 4Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Ontario, Canada*These authors contributed equally to this workCorrespondence: Elizabeth Alvarez, Department of Health Research Methods, Evidence and Impact, McMaster University, CRL 2nd Floor, 1280 Main Street West, Hamilton, Ontario, L8S 4K1, Canada, Tel +1 905 525-9140 Ext. 22248, Email alvare@mcmaster.caBackground: Cannabis for medical purposes has been legal in Canada since 2001; however, physicians receive no formal training in this modality, and clinical use of cannabis remains controversial. This study aims to explore the values and preferences of people living with chronic pain (PLwCP) in using medical cannabis for chronic pain to inform guideline development and shared decision-making in clinical practice.Methods: We conducted a descriptive qualitative study using in-depth interviews with PLwCP. Using a deductive/inductive approach, we developed concepts and themes related to values and preferences of PLwCP on their use (or avoidance) of medical cannabis for chronic pain.Results: We interviewed 52 PLwCP, including current medical cannabis users (40), previous users (10) and non-users (2). Most PLwCP who used cannabis therapeutically reported the need for experimentation to determine what cannabis products, routes, and doses worked for them. Perceived benefits of medical cannabis among current users included relief from pain, better sleep, and improved mental health. Reasons for discontinuing use of medical cannabis included lack of improvement in pain or sleep or undesirable side effects. Cannabidiol (CBD) dominant products were reported to result in minimal adverse effects (eg, physical or mental impairment) compared to tetrahydrocannabinol (THC) dominant products. Perceived barriers or facilitators to use included social acceptability, availability or access, cost, and attitudes and knowledge among healthcare providers. Participants noted different routes of cannabis use including oral routes that provided longer-lasting pain relief with a slower onset and inhaled routes with a more rapid onset with shorter-lived effects.Conclusion: Participantsâ decisions to use medical cannabis for chronic pain were varied, which suggests these decisions are likely to be sensitive to individualsâ values and preferences. There is a call for further research and information-sharing to help PLwCP understand the complexities of cannabis use for medical purposes, including ideal dosing and timing.Plain Language Summary: In Canada, cannabis for medical reasons has been legal since 2001. It has been used as one of the many strategies for chronic or ongoing pain, but doctors are not given consistent information regarding its use, and existing guidance does not include the patient point of view. We did this study to explore how people living with chronic pain feel about the use of medical cannabis. We asked 52 people living with chronic pain, including current medical cannabis users, previous users, and non-users. We found that many people who used cannabis for their pain had to experiment to determine what cannabis products, routes, and doses worked for them. Benefits of medical cannabis included relief from pain, better sleep, and improved mental health. Reasons for stopping medical cannabis included no to little improvement in pain and/or sleep or the presence of unwanted side effects. Cannabidiol (CBD) products resulted in fewer unwanted effects (eg, physical or mental impairment) compared to tetrahydrocannabinol (THC) products. People discussed different routes of cannabis use including oral routes that provided longer-lasting pain relief but with a slower onset and inhaled routes with a faster onset of relief but with shorter-lived effects. Peopleâs decisions regarding medical cannabis use for chronic pain were varied, suggesting these decisions are likely to be sensitive to individualâs values and preferences. More research is needed to learn what doses, products, and routes work for specific chronic pain conditions.Keywords: medical cannabis, chronic pain, health services, guideline, patient values and preferences, Canada
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".