The model of care at a leading medical cannabis clinic in Canada
Bibliographic record
Abstract
Medical cannabis access has been legalized in more than 30 countries worldwide and popularity among patients is increasing rapidly. Cannabinoid-based treatments have been shown to be beneficial for several symptoms such as chemotherapy-induced nausea and vomiting, spasticity, chronic pain, intractable seizures and insomnia, yet high-quality clinical trials are still limited. As millions of patients now have legal access to medical cannabis, little information is available about the development of best clinical practices and an effective medical cannabis clinic model. A medical cannabis clinic is an innovative and emergent practice model that may be necessary to bridge the gap between patient and healthcare provider interest and existing barriers to the prescription of medical cannabis treatments, such as limited medical education, lack of high-quality clinical research and challenging or evolving regulatory frameworks. In this paper, we describe the model of care and organization of a dedicated medical cannabis clinic operating in Quebec, Canada since 2014. We share the principles of medical cannabis practice, including the structure of its medical and support team, clinic organisation and procedure guidelines. Key clinic statistics and patient demographics are shared with year by year comparison. Operating since 2014, the clinic has endured a rapidly changing regulatory landscape in Canada, overcoming numerous challenges including medical and social stigma, limited funding, resources and institutional support combined with a high demand for services. To support medical cannabis leaders globally, an important knowledge-sharing is required. The clinic has expanded to a network of four clinic sites across Quebec and offers continuing education and preceptorships to health care providers and trainees as well as research services to both academic and industry partners. The description of the clinic offers guidance on medical cannabis treatment and care and discusses possible solutions to associated challenges. The clinic model of care can be adapted to different healthcare settings and regulatory frameworks; it may assist physicians and health care providers in the development of medical cannabis clinics or the implementation of best practices as medical cannabis access continues to evolve.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".