The application of a new policy on recreational cannabis use in the Canadian military population
Bibliographic record
Abstract
Abstract In October 2018, the Canadian federal government passed the Cannabis Act, making Canada the first G7 nation to legalize the possession and consumption of recreational cannabis nationwide. Given the potential health and safety implications, this makes it a pivotal time for both policy makers and researchers to understand the effects of cannabis use and the implications in the workplace. In response to legalization, the Canadian Armed Forces (CAF) was one of the first major organizations to create a policy specifically targeting cannabis use for its military personnel, with directives outlining expectations for standards of conduct, and general and specific prohibitions relating to the possession and consumption of cannabis products. An important part of understanding the potential impact of cannabis use in the Canadian military and the suitability of the current policy is to first determine the proportion of CAF members that have consumed cannabis since its legalization. The first available data on this comes from the Recruit Health Questionnaire, a health and lifestyle survey completed by military recruits early in their basic training. 1,219 recruits completed the survey between October 2019 (exactly one year post-legalization) and February 2020 (88% response rate). Preliminary results suggest that one-third (34%) of recruits reported having consumed cannabis during the 12 months prior to starting basic training, and since the Cannabis Act came into effect. The vast majority (95%) of this group reported having consumed cannabis on more than one occasion. Given these large proportions, it is imperative that we continue to study the medical, behavioural, and cognitive effects of cannabis to understand if the current guidelines set out in the CAF policy on recreational cannabis use are adequate. Constant monitoring and, as necessary, updates to the policy, are essential to continue ensuring the safety of CAF members and the public, and an operationally ready force. Key messages Preliminary results suggest that one-third of Canadian Armed Forces recruits have consumed cannabis since its legalization, with 95% of this group having consumed it on more than one occasion. Given the potential health and safety implications, the Canadian Armed Forces policy on cannabis consumption must be constantly monitored and updated to ensure a safe and operationally ready force.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".