Evaluating the impact of Canadian cannabis legalization on cannabis use outcomes in emerging adults: Comparisons to a US control sample via a natural experiment
Bibliographic record
Abstract
BACKGROUND: Recreational cannabis legalization marked a significant policy shift in Canada, but has been difficult to evaluate because of the absence of a control group. Although it is unfeasible to evaluate legalization using a randomized controlled trial design, sophisticated statistical techniques can employ quasi-experimental designs using natural experiments. This study evaluates the impact of cannabis legalization in a longitudinal cohort of Canadian emerging adults by comparing changes in cannabis use frequency and related consequences over time to changes in a similar cohort in a United States jurisdiction where no policy change took place. METHODS: Two samples of emerging adults from Hamilton, Ontario, and Memphis, Tennessee, were followed longitudinally in 4-month intervals from March 16, 2018 to March 11, 2020, with three pre-legalization and four post-legalization assessments. Doubly robust difference-in-difference (DiD) estimation was used to assess whether cannabis legalization impacted cannabis use frequency or cannabis-related consequences in the Canadian sample over time. The impact of cannabis legalization on alcohol use and alcohol-related consequences was also assessed as a control form of substance use for which no policy change took place. Cohort differences were adjusted within DiD estimation using propensity score balancing. RESULTS: Against a general trend of decreasing use over time, the DiD estimation revealed significantly greater cannabis use frequency approximately 6-months post legalization (ATT (95% CI): 0.2245 (0.0154, 0.4336)) and approximately one year post legalization (ATT (95% CI):0.3091 (0.0473, 0.5709)) in the Canadian sample compared to the American sample. Cannabis-related consequences were also greater in the Canadian sample at both of these time points (ATT (95% CI): 0.0.7610 (0.0797, 1.4423)), (ATT (95% CI): 1.0396 (0.1864, 1.8928)). These higher levels reflected less steep declines over time (i.e., attenuated 'aging out'). Alcohol changes showed no impact of legalization at any time point, as expected. CONCLUSIONS: Findings suggest that cannabis legalization was associated with smaller reductions in cannabis use frequency and adverse consequences than expected in the Canadian sample compared to the American control sample. Although the magnitude of these impacts was small, these findings suggest the start of diverging cannabis trajectories. Given that effects of legalization are hypothesized to be long-term rather than immediate, further monitoring of the impacts of cannabis legalization on developmental trends in cannabis use and related consequences is warranted.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".