Correlates of Perceived Harmfulness of Regular Cannabis Use among Canadian University Students Before and After Legalization
Bibliographic record
Abstract
Objective: Among a prospective sample of Canadian university students, this study aimed to: 1) document changes in cannabis use and perceived harmfulness of use before and after the legalization of recreational cannabis; 2) examine correlates of perceived harmfulness; and 3) explore changes in perceived harmfulness as a function of cannabis use patterns. Method: A random sample of 871 students at one western Canadian university were assessed pre- and post-legalization of recreational cannabis. Descriptive and inferential statistics were used to explore changes in cannabis use and perceived harmfulness. A random effects model was developed to assess whether cannabis legalization was associated with perceptions of harmfulness of regular cannabis use. Results: Twenty-six percent of the sample used cannabis during the past three months at both timepoints. The majority of the sample perceived regular cannabis use as a high-risk behaviour at each timepoint (57.3% and 60.9%, respectively). Results from the random effects model showed that after controlling for covariates, cannabis legalization was not associated with changes in perceived harmfulness. Perceptions of harm remained relatively stable regardless of cannabis use pattern. Respondents who endorsed cannabis use at both timepoints reported a significant increase in their frequency of cannabis use post-legalization. Conclusions: Legalization of cannabis for recreational use was not associated with substantive changes in perceptions of harm among post-secondary students, yet it might lead to increases in cannabis use among those who already use the substance. Ongoing monitoring of policies is needed, as are targeted public health initiatives to identify post-secondary students who are at risk for cannabis-related consequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".