Associations between noticing public health education campaigns about cannabis and risk perceptions in the northern Canadian territories: a cross-sectional study
Bibliographic record
Abstract
This study investigated whether noticing cannabis education campaigns was associated with increased cannabis risk perceptions in Canada's three territories following non-medical cannabis legalization. Data were from the Cannabis Policy Study in the Territories, including 2452 participants, age ≥16 years residing in Yukon, Northwest Territories and Nunavut. Poisson regression with robust standard errors were used to estimate associations between noticing cannabis education campaigns and moderate to very high risk perceptions of daily cannabis smoking, vaping, edible use and exposure to second-hand cannabis smoke, adjusting for sociodemographic characteristics and cannabis-use frequency. Results were compared with associations with risk perceptions of daily alcohol consumption and cigarette smoking, not included in cannabis education campaigns. Interactions were examined between noticing education campaigns and age group and cannabis-use frequency. Cannabis education campaigns were noticed by 40.4% of respondents, with lower awareness among those with lower education and income. Noticing campaigns was associated with higher risk perceptions of daily cannabis smoking [adjusted risk ratio (RRadj) = 1.09, 95% confidence interval (CI): 1.02-1.16] and vaping (RRadj = 1.09, 95%CI: 1.02-1.16). Significant interactions were not found with age group or cannabis-use frequency. Findings are consistent with modest effects of cannabis education campaigns. Approaches are needed to increase reach of cannabis education campaigns, including among groups with lower education and income.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".