Informed Cannabis Policies on Canadian Campuses: Toward the Protection of Youth and Young Adults
Bibliographic record
Abstract
Introduction: The legalization of cannabis across Canada in October 2018 introduced issues including regulation at different levels, public and individual education, and discussions about cannabis product safety. We aimed to discuss ineffective and effective cannabis use policy on campuses and associated public areas, given the known short-term and long-term effects pertaining to its neurologic, pulmonary, and purported medicinal effects. Cannabis interferes with many of the body’s basic and executive (higher-level) functions. It is also associated with long-term harmful effects when chronically used. The purpose of this paper is to review and further discuss the responsibility local governments and educational institutions have for creating policies and regulations around cannabis use, particularly within post-secondary institutions, and for implementing educational strategies to promote public knowledge of cannabis. Methods: Peer-reviewed articles published in the last 10 years were searched for through the MEDLINE database. In addition, national and local health-related websites discussing cannabis policies were reviewed and collated. Expert opinions were also sought out to provide further information and resources. Results: 31 peer-reviewed articles and 12 professional websites were retrieved and reviewed. Correspondences with individual experts aware of and involved with campus cannabis policies also provided relevant resources and data used in this document. Conclusion: Smoke-free campus policies create the best health outcomes for the campus population. As well, creating effective and properly regulated policies and prioritizing public education is pertinent especially on universities where the population demographic is relatively young.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".