Medical Cannabis Use Among Canadian Veterans and Non-Veterans: A National Survey
Bibliographic record
Abstract
Background: Medical cannabis (MC) is used by Canadian Veterans to manage a wide range of health issues. However, there is little information comparing the reasons for MC use and its perceived effectiveness between Veterans and non-Veterans. Objects: We compared MC use among a convenience sample of Canadian Veterans and with non-Veteran controls, including demographics, reasons and patterns of use, and perceived effectiveness. Methods: Between November and December 2021, Canadian Veterans using cannabis were invited to participate in a survey using a national press release, social media, and announcements on online platform dedicated to promoting health among Canadian Veterans and non-Veterans during the pandemic (www.MissionVav.com). The survey was also mentioned in a monthly newsletter from Veteran Affairs Canada. Self-reported effectiveness was evaluated using a 0 to 10 visual analogue scale (0 being not all effective, 10 being the most effective). Results: The survey was completed by 157 people, including 108 (69%) males and 49 (31%) females. The mean age was 57 years (range 19 to 84). Among responders, 90 (63%) identified as Veterans. The most common reasons for MC use among Veterans included: insomnia (80%), anxiety (73%), and depression (52%). Veterans reported medical conditions such as chronic pain (88%) and arthritis (51%). Compared with non-Veterans, Veterans were significantly more likely to be male (83% vs. 49%), have a higher BMI (35.2 vs. 30.9), to report problems with sleep, anxiety, depression, and PTSD, and to use cannabis in edible form (51% vs. 22%). Self-reported mean effectiveness scores for MC were highest for PTSD (8.4), insomnia (8.2), anxiety (8.1), depression (8.0), and chronic pain (7.6). Conclusions: We found important differences in user characteristics and cannabis use patterns between Canadian Veterans and non-Veterans. Further controlled studies are required to validate these findings, but these data suggest that orally administered cannabis products may be worth further study.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".