Predictors of Change in Cannabis use Status From Pre- to Post-Recreational Cannabis Legalization in Canada: Evidence From a Two-Wave Longitudinal National Survey
Bibliographic record
Abstract
Objective: In October 2018, the Government of Canada legalized cannabis for recreational use nationwide. The effects of legalization on cannabis use have been primarily assessed through cross-sectional surveys. Method: In the present study, a two-wave longitudinal design was used to explore potential demographic, substance use and behavioral addiction, and mental health predictors of change in cannabis use status following legalization. Canadian online panelists (18+) were initially surveyed about their gambling and substance use in 2018 (i.e., before cannabis legalization). From the original sample, 4,707 (46.2%) were retained in the follow-up survey one year later, post-cannabis legalization. These respondents were the focus of the present study. Results: When queried about how legalization would impact their use, 61.8% said, 'I'll never use it', 21.1% stated "I'll use it about the same as I do now," 10.3% indicated, "I may try it for the first time," 5.0% answered, "I'll use it more," and 1.9% responded that, "I'll use it less." Consistent with these sentiments, within the retained sample there was a modest but significant increase in cannabis use from baseline (18.4%) to follow-up (26.1%). Regressions established that younger age, being male, substance use, tobacco or e-cigarette use, problematic gambling, and stated intention to use cannabis were predictors of later cannabis use. Conclusions: This national cohort design indicates that cannabis use appears to have increased in Canada following legalization. The present study makes a unique contribution by also identifying variables that statistically forecast movement toward and away from cannabis use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".