Medical cannabis use in Canada: vapourization and modes of delivery
Bibliographic record
Abstract
BACKGROUND: The mode of medical cannabis delivery-whether cannabis is smoked, vapourized, or consumed orally-may have important implications for its therapeutic efficacy and health risks. However, there is very little evidence on current patterns of use among Canadian medical cannabis users, particularly with respect to modes of delivery. The current study examined modes of medical cannabis delivery following regulatory changes in 2014 governing how Canadians access medical cannabis. METHODS: A total of 364 approved adult Canadian medical cannabis users completed an online cross-sectional survey between April and June 2015. The survey examined patterns of medical cannabis use, modes of delivery used, and reasons for use. Participants were recruited through a convenience sample from nine Health Canada licensed producers. RESULTS: Using a vapourizer was the most popular mode of delivery for medical cannabis (53 %), followed by smoking a joint (47 %). The main reason for using a vapourizer was to reduce negative health consequences associated with smoking. A majority of current vapourizer users reported using a portable vapourizer (67.2 %), followed by a stationary vapourizer (41.7 %), and an e-cigarette or vape pen (19.3 %). Current use of a vapourizer was associated with fewer respiratory symptoms (AOR = 1.28, 95 % CI 1.05-1.56, p = 0.01). CONCLUSIONS: The findings suggest an increase in the popularity of vapourizers as the primary mode of delivery among approved medical users. Using vapourizers has the potential to prevent some of the adverse respiratory health consequences associated with smoking and may serve as an effective harm reduction method. Monitoring implications of such current and future changes to medical cannabis regulations may be beneficial to policymakers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".