Comparison of Indicators of Dependence for Vaping and Smoking: Trends Between 2017 and 2022 Among Youth in Canada, England, and the United States
Bibliographic record
Abstract
INTRODUCTION: The current study sought to examine trends in indicators of dependence for youth vaping and smoking during a period of rapid evolution in the e-cigarette market. AIMS AND METHODS: Data are from repeat cross-sectional online surveys conducted between 2017 and 2022 among youth aged 16-19 in Canada, England, and the United States (US). Participants were 23 145 respondents who vaped and/or smoked in the past 30 days. Four dependence indicators were assessed for smoking and vaping (perceived addiction, frequent strong urges, time to first use after waking, days used in past month) and two for vaping only (use events per day, e-cigarette dependence scale). Regression models examined differences by survey wave and country, adjusting for sex, age, race, and exclusive/dual use. RESULTS: All six indicators of dependence increased between 2017 and 2022 among youth who vaped in the past 30 days (p < .001 for all). For example, more youth reported strong urges to vape at least most days in 2022 than in 2017 (Canada: 26.5% to 53.4%; England: 25.5% to 45.4%; US: 31.6% to 50.3%). In 2017, indicators of vaping dependence were substantially lower than for smoking; however, by 2022, youth vaping was associated with a greater number of days used in the past month (Canada, US), shorter time to first use (all countries), and a higher likelihood of frequent strong urges (Canada, US) compared to youth smoking. CONCLUSIONS: From 2017 to 2022, indicators of vaping dependence increased substantially. By 2022, vaping dependence indices were comparable to those of smoking. IMPLICATIONS: Indicators of vaping dependence among youth have increased substantially since 2017 to levels that are comparable to cigarette dependence among youth who smoke. Future research should examine factors underlying the increase in dependence among youth who vape, including changes to the nicotine profile and design of e-cigarette products.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".