Trends in E-Cigarette and Tobacco Cigarette Purchasing Behaviors by Youth in the United States, Canada, and England, 2017–2022
Bibliographic record
Abstract
Objectives: This paper describes trends in youth e-cigarette (EC) and tobacco cigarette (TC) purchasing behaviors in Canada, England, and the United States (US) in relationship to changing minimum legal age (MLA) laws. Methods: Data are from eight cross-sectional online surveys among national samples of 16- to 19-year-olds in Canada, England, and the US conducted from 2017 to 2022 (N = 104,467). Average wave percentage change in EC and TC purchasing prevalence and purchase locations were estimated using Joinpoint regressions. Results: EC purchasing increased between 2017 and 2022, although the pattern of change differed by country. EC purchasing plateaued in 2019 for the US and in 2020 for Canada, while increasing through 2022 for England. TC purchasing declined sharply in the US, with purchasing from traditional retail locations declining, while purchasing from social sources increased. Vape shops were the most common location for EC purchasing, although declining in England and the US. Conclusion: Trends in EC and TC purchasing trends in the US are consistent with the expected impact of the federal MLA law increasing the legal age to 21 years in December 2019.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".