Trends and Socio-Demographic Differences of Cannabis Vaping in the USA and Canada
Bibliographic record
Abstract
Given the rise in cannabis vaping, it is important to highlight the heterogeneity in vaping different cannabis product because of the potential differences in their health risks. This study aims to estimate the trends and socio-demographic correlates of the use of various cannabis vaping products across jurisdiction with different legal status. Data from the 2018 (n = 27,169) and 2019 (n = 47,747) waves of the International Cannabis Policy Study (ICPS) were used. Respondents aged 16–65 completed web-based surveys. In 2019, proportions of past year vaping of cannabis oil, dried flower and concentrates in the overall sample were highest in U.S. jurisdictions where cannabis was legalized for non-medical use (17.4%, 6.0%, 4.9%), followed by U.S. jurisdiction where non-medical cannabis use is illegal (13.7%, 5.8%, 2.9%), and lowest in Canada (8.1%, 4.4%, 2.1%). Vaping dried flower decreased from 2019 to 2018 in U.S. legal jurisdictions and Canada, while vaping cannabis oil and concentrates increased in all jurisdictions (p < 0.001). The odds of vaping all forms of products were higher among younger respondents (16–55 years), males, respondents with some college education, and persons with low-risk perceptions on daily cannabis vaping. In both ICPS surveys (2018 and 2019), cannabis oil was the most frequently vaped products, followed by dried flower, and concentrates. Detailed measures of product forms for cannabis vaping should be considered in future surveys.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".