Ever Use of Nicotine and Nonnicotine Electronic Cigarettes Among High School Students in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: There are limited data on the use of electronic cigarettes (e-cigarettes) among youth, particularly with regard to the use of nicotine versus nonnicotine products. This study investigates ever use of nicotine and nonnicotine e-cigarettes and examines the demographic and behavioral correlates of e-cigarette use in Ontario, Canada. METHODS: Data for 2,892 high school students were derived from the 2013 Ontario Student Drug Use and Health Survey. This province-wide school-based survey is based on a 2-stage cluster design. Bivariate and multivariate analyses were used to investigate the factors associated with ever use of e-cigarettes. Ever use of e-cigarettes was derived from the question, "Have you ever smoked at least one puff from an electronic cigarette?" All analyses included appropriate adjustments for the complex study design. RESULTS: Fifteen percent of high school students reported using e-cigarettes in their lifetime. Most students who ever used e-cigarettes reported using e-cigarettes without nicotine (72%), but 28% had used e-cigarettes with nicotine. Male, White/Caucasian, and rural students, as well as those with a history of using tobacco cigarettes, were at greater odds of e-cigarette use. Seven percent of students who had never smoked a tobacco cigarette in their lifetime reported using an e-cigarette. Five percent of those who had ever used an e-cigarette had never smoked a tobacco cigarette. CONCLUSION: More students reported ever using e-cigarettes without nicotine than with nicotine in Ontario, Canada. This underscores the need for greater knowledge of the contents of both nicotine and nonnicotine e-cigarettes to better guide public health policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".