Age-related Patterns of Medical Cannabis Use: A Survey of Authorized Patients in Canada
Bibliographic record
Abstract
Objective: An increasing number of Canadians are registering as authorized users of medical cannabis. Older patients comprise a growing subset of this population; however, relatively little information exists around age-related patterns of medical cannabis use. Methods: The Canadian Cannabis Patient Survey (CCPS) is a large cross-sectional survey of authorized medical cannabis patients in Canada. This publication summarizes the results of the CCPS 2021, with a focus on age-related outcomes and the elderly sub-population. Results: The survey was completed by 2,697 patients. The mean age of participants was 54.3 years of age and the proportion of female respondents was 49.1%. Among older patients, pain was the most common symptom, while anxiety was the most common symptom reported by younger patients. Older patients exhibited a significant preference for oral administration over inhalation of medical cannabis when compared to younger patients, respectively (p>0.05). Among patients taking prescription opioids, most of whom were older patients, 54% reported a decrease in use concurrent with medical cannabis. Conclusions: Older patients comprise a growing subset of medical cannabis patients, which is also reflected in CCPS participants over time. This patient population exhibits different patterns of use compared to their younger counterparts, preferring high CBD orally ingested formulations, which they use primarily to treat pain-related illnesses/symptoms. Overall, study participants reported that cannabis had a high degree of efficacy in alleviating their illness/symptoms, and many reported a reduction in their use of prescription opioids, alcohol, tobacco, and other substances.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".