Use of flavored cannabis vaping products in the US, Canada, Australia, and New Zealand: findings from the international cannabis policy study wave 4 (2021)
Bibliographic record
Abstract
Background: Vaping is an increasingly popular mode of cannabis use. Few studies have characterized the role of flavors in cannabis e-liquids.Objectives: To explore the prevalence of flavored vaping liquids, including differences between countries and correlates of use.Methods: Data were from Wave 4 (2021) of the International Cannabis Policy Study with national samples aged 16–65 in Canada, the United States (US), Australia, and New Zealand. The sample comprised 52,938 respondents, including 6,265 who vaped cannabis e-liquids in the past 12-months (2,858 females, 3,407 males). Logistic regression models examined differences in the use of flavored e-liquids between countries and sociodemographic characteristics.Results: The prevalence of vaping cannabis e-liquids was highest in the US (15.3%) and Canada (10.7%) compared to Australia (4.0%) and New Zealand (3.7%). Among past 12-month cannabis consumers, 57.5% reported using flavored vaping liquids, 34.2% used unflavored vaping products and 8.3% did not know. People who vape in Australia were most likely to report using flavored liquids compared to New Zealand (OR = 2.29), Canada (OR = 3.14), and the US (OR = 3.14) (p < .05 for all). Fruit was the most reported vaping flavor (40.8%), followed by candy/dessert (20.4%) and vanilla (15.2%). Use of flavored vapes was greater among younger, ethnic minorities, female, higher education and income adequacy, and more frequent consumers (p < .05).Conclusion: Many cannabis consumers reported using flavored e-liquids, with highest levels among young people aged 16–35. Given the high prevalence of vaping in legal markets, regulators should consider the role of flavored vaping products in promoting cannabis use among this group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".