Reducing medical cannabis use risk among Veterans: A descriptive study
Bibliographic record
Abstract
BACKGROUND: Canadian Veterans experiencing chronic pain report concerns about accessing accurate information on the risks associated with medical cannabis (MC) use. The Lower Risk Cannabis Use Guidelines (LRCUG) were developed to equip individuals who use cannabis recreationally with safer-use strategies. Many of the harm reduction recommendations for recreational cannabis use are relevant and important considerations for MC use. The primary objective of our study was to assess Canadian Veterans' awareness of and interest in the LRCUG, and engagement in potential higher-risk MC use behaviours. METHODS: Canadian Armed Forces Veterans living with chronic pain (N = 582) were recruited online and through the Chronic Pain Centre of Excellence for Canadian Veterans. Participants completed measures on: cannabis use (never, past, current use), sources of cannabis knowledge, mental health, and awareness of and interest in receiving the LRCUG. Chi-Square and post-hoc analyses characterized the sample and assessed for demographic differences based on cannabis use status and awareness of the LRCUG. Engagement in higher-risk MC use behaviours were aligned to LRCUG recommendations, and detailed descriptively. RESULTS: Veterans who currently use cannabis were more likely to be unemployed (z = 3.62, p < .01), released as a Non-Commissioned Officer (z = -3.83, p < .01), and unable to work due a disability (z = -3.43, p < .01) than Veterans who do not currently use. Less than 30% of Veterans were aware of the LRCUG, with greater awareness among individuals who currently use cannabis (n = 356). Engagement in higher-risk MC use behaviours that contradicted LRCUG recommendations ranged from ~ 9% to ~ 85%. Approximately 9% of Veterans experienced co-morbid mental health concerns, yet their MC use was not for mental health purposes (LRCUG recommendation #7). Additionally, almost 85% of Veterans engaged in daily MC use (LRCUG recommendation #5). The majority of Veterans who currently use cannabis engaged in two or more higher-risk MC use behaviours (60.2%; LRCUG recommendation #12). Almost half of all Veterans received their cannabis information from a healthcare provider or the internet. CONCLUSIONS: Our study suggests the importance of safer use guidelines tailored for MC use. Development of lower-risk MC use guidelines can support prescribing practitioners and Veterans with information needed for safer and better-informed MC use decisions, tailored to patients' needs and circumstances.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".