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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".