Co-use of cannabis and alcohol before and after Canada legalized nonmedical cannabis: A repeat cross-sectional study
Bibliographic record
Abstract
BACKGROUND: This study examined changes in population-level co-use of cannabis and alcohol before and 12 months after nonmedical cannabis legalization in Canada, relative to the United States that had previously legalized and not legalized (US legal and illegal states, respectively). METHODS: = 19,626) states completed an online survey. Changes in co-use between 2018 and 2019 in US legal and illegal states compared to those in Canada were assessed using multinomial logistic regression. RESULTS: Descriptive analyses show increases in cannabis use and monthly or more frequent (MMF) co-use between 2018 and 2019 in all jurisdictions. Compared to no MMF use of cannabis or alcohol, there was no evidence suggesting differences in changes in MMF co-use in US legal or illegal states relative to Canada. However, respondents in US legal states had 33% higher odds of MMF alcohol-only use (OR = 1.33, 99% CI: 1.12, 1.57) compared to no MMF use relative to Canada. CONCLUSIONS: Increases in co-use were observed between 2018 and 2019 in all jurisdictions regardless of the legal status of cannabis. These shifts were largely due to increases in cannabis use across the population, including those that use alcohol, and may indicate changing societal norms toward cannabis generally. As the cannabis legalization transition in Canada matures, evaluation over the longer term will improve understanding of the influence of cannabis liberalization on co-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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".