The Lower-Risk Cannabis Use Guidelines’ (LRCUG) recommendations: How are Canadian cannabis users complying?
Bibliographic record
Abstract
Canada, alongside other jurisdictions, implemented non-medical cannabis legalization in 2018, partly towards improving public health. Evidence-based 'Lower-Risk Cannabis Use Guidelines' (LRCUG), including recommendations for cannabis users on how to decrease risk-behaviors for harms, have been developed and widely disseminated in Canada since 2017. However, knowledge on users' compliance with the LRCUG is limited. We identified four major Canadian (three national, one provincial) population surveys presenting key data on cannabis-related behaviors: the National Cannabis Survey, Canadian Cannabis Survey, Canadian Tobacco, Alcohol & Drugs Survey, and CAMH Monitor. We scanned each survey for indicator data mapping onto either of the LRCUG's recommendations for the years 2017 to 2019. Relevant indicator data, albeit with varying operationalizations, were found for six of the ten LRCUG's recommendation clusters in at least some of the surveys, and were extracted and summarized. For results, substantial -- but declining -- majorities of users consumed cannabis by smoking, yet with shifts towards other use modes. Between one- to two-in-five users engaged in the risk-behaviors of using high-potency cannabis products, frequent cannabis use and cannabis-impaired driving, respectively. A small proportion of pregnant or breastfeeding women continued cannabis use during the study period. The data identified found suggested a heterogeneous picture regarding cannabis users' compliance with the LRCUG's recommendations. Non-compliance is highest for recommendations regarding modes-of-use, and applies to minorities of users for other risks factors. These sub-groups are at elevated risk for acute (e.g., accidents) or long-term (e.g., dependence) cannabis-related harms contributing to the public health burden. Appropriate targeted interventions in these areas require improvement.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".