LITERATURE REVIEW Lower Risk Cannabis Use Guidelines for Canada (LRCUG): A Narrative Review of Evidence and Recommendations
Bibliographic record
Abstract
Objectives: More than one in ten adults – and about one in three young adults – report past year cannabis use in Canada. While cannabis use is associated with a variety of health risks, current policy prohibits all use, rather than adopting a public health approach focusing on interventions to address specific risks and harms as do policies for alcohol. The objective of this paper was to develop ‘Lower Risk Cannabis Use Guidelines ’ (LRCUG) based on research evidence on the adverse health effects of cannabis and factors that appear to modify the risk of these harms. Methods: Relevant English-language peer-reviewed publications on health harms of cannabis use were reviewed and LRCUG were drafted by the authors on the basis of a consensus process. Synthesis: The review suggested that health harms related to cannabis use increase with intensity of use although the risk curve is not well characterized. These harms are associated with a number of potentially modifiable factors related to: frequency of use; early onset of use; driving after using cannabis; methods and practices of use and substance potency; and characteristics of specific populations. LRCUG recommending ways to reduce risks related to cannabis use on an individual and population level – analogous to ‘Low Risk Drinking Guidelines ’ for alcohol – are presented. Conclusions: Given the prevalence and age distribution of cannabis use in Canada, a public health approach to cannabis use is overdue. LRCUG constitute a potentially valuable tool in facilitating a reduction of health harms from cannabis use on a population level. Key words: Canada; cannabis; epidemiology; morbidity; policy; public health La traduction du résumé se trouve à la fin de l’article. Can J Public Health 2011;102(5):324-27.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".