E-cigarette consumption among youth who vape in Canada, England, New Zealand and the USA: Exploring methods to quantify consumption amounts and differences by product attributes using population-level surveys
Bibliographic record
Abstract
SIGNIFICANCE: Despite the popularity of vaping among young people, data on e-liquid consumption remain limited. The current study explores methods to quantify e-liquid consumption among youth who currently vape in four countries. METHODS: Data were analysed from the 2023 International Tobacco Control Policy Evaluation Project Youth Surveys, conducted online with national samples in Canada, England, New Zealand and the USA, among 2916 youth aged 16-19 who vaped in the past 30 days. The volume of e-liquid consumed in the past 30 days was estimated from device-specific measures. Linear regression models examined differences in total e-liquid consumption by (1) country, age, sex-at-birth, exclusive versus dual vaping/smoking and device type; (2) four vaping dependence variables (frequency of strong urges, perceived addiction, days vaped, E-cigarette Dependence Scale (EDS) score) and (3) flavour. RESULTS: Across countries, total e-liquid consumption reported in the past 30 days was a median of 9.7 mL and a mean of 22.4 mL. Compared with the USA, e-liquid consumption was greater in Canada (β=4.6, p=0.048) and England (β=4.8, p=0.027). Using multiple device types was associated with greater e-liquid consumption (eg, three device types vs only pods/cartridges: β=54.6, p<0.001). All four dependence indicators were positively associated with consumption, including urges to vape, perceived addiction, days vaped and EDS (all p<0.001). Youth who vaped fruit flavours reported the greatest e-liquid consumption (β=9.1, p=0.001), with some evidence of higher consumption levels for sweet/drinks/other flavours (β=4.3, p=0.093). CONCLUSIONS: The findings suggest substantial e-liquid consumption among youth who vape in all four countries.
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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 | 0.000 |
| 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".