Indicators of dependence and efforts to quit vaping and smoking among youth in Canada, England and the USA
Bibliographic record
Abstract
OBJECTIVE: The current study examined indicators of dependence among youth cigarette smokers and e-cigarette users in Canada, England and the USA, including changes between 2017 and 2019. METHODS: Data are from repeated cross-sectional online surveys conducted in 2017, 2018 and 2019 with national samples of youth aged 16-19 years, in Canada (n=12 018), England (n=11 362) and the USA (n=12 110). Measures included perceived addiction to cigarettes/e-cigarettes, frequency of experiencing strong urges to smoke/use an e-cigarette, plans to quit smoking/using e-cigarettes and past attempts to quit. Logistic regression models were fitted to examine differences between countries and changes over time. RESULTS: The proportion of ever-users who vaped frequently was significantly higher in 2019 compared with 2017 for all outcomes in each country. Between 2017 and 2019, the proportion of past 30-day vapers reporting strong urges to vape on most days or more often increased in each country (Canada: 35.3%, adjusted OR (AOR) 1.69, 95% CI 1.20 to 2.38; England: 32.8%, AOR 1.55, 1.08 to 2.23; USA: 46.1%, AOR 1.88, 1.41 to 2.50), along with perceptions of being 'a little' or 'very addicted' to e-cigarettes (Canada: 48.3%, AOR 1.99, 1.44 to 2.75; England: 40.1%, AOR 1.44, 1.03 to 2.01; USA: 53.1%, AOR 1.99, 1.50 to 2.63). Indicators of dependence among smokers were consistently greater than e-cigarette users, although differences had narrowed by 2019, particularly in Canada and the USA. CONCLUSIONS: Prevalence of dependence symptoms among young e-cigarette users increased between 2017 and 2019, more so in Canada and the USA compared with England. Dependence symptom prevalence was lower for e-cigarettes than smoking; however, the gap has narrowed over time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".