Public Health Concerns of Cannabis in Canada: Trends in Public Opinion Before and After Legalization
Bibliographic record
Abstract
With the legalization and regulation of recreational cannabis in Canada coming into effect on October 17th, 2018, Canada became just the second country in the world to legalize the longstanding prohibited substance after first being outlawed in 1923. While public opinion throughout the country had favoured the adoption of drug-law reformation for some time, limited data existed on the health-related implications and public perceptions of cannabis use before the legislation was introduced. With little to no well-documented evidence available to base their own public policy decisions on, the federal government under newly elected Prime Minister Justin Trudeau and the Liberal Party of Canada outlined several principal objectives when committing to legalizing, regulating and restricting access to cannabis in Canada. The federal government recognised the best tactic as being a public health approach which prioritized decision making for the new regulatory system based on features that should uphold and promote the health and safety of Canadians. This research paper seeks to reflect on the effectiveness of the outlined policy objectives through public opinion by analyzing changes in annual trends pertaining to cannabis associated risks and harms. Three key areas are discussed using data from the Canadian Cannabis Survey (2017-2020) including perceptions of cannabis as being habit forming; cannabis associated risks among other substances; and opinions of cannabis specific harms. This paper ultimately argues that increased exposure to mandatory health warnings and realized effects of cannabis use increased the negative perception of cannabis smoke as being harmful, young adults as being most at risk, and cannabis as being a habit-forming substance while reducing the negative perception of cannabis compared to other substances and its effects on mental health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".