134: Patterns and Associated Factors with E-Cigarette Use Among Canadian Adolescents
Bibliographic record
Abstract
Electronic-cigarettes (e-cigarettes) have been marketed as a smoking cessation tool. Use amongst adolescents has not been well-described. To evaluate the prevalence, motivations, and risk factors for use of e-cigarettes in adolescents. A population-based, cross-sectional universal screening study was performed in the geographically and administratively defined Niagara Region, Ontario, Canada. Data were collected from all grade-9 students through the Heart Niagara Inc. Healthy Heart Schools' Program (2013–2014 school year). Questionnaires assessed cigarette, e-cigarette, and other tobacco use. Household income was assessed through the 2011 Canadian census. N = 2367 students (54% male, 14.6±0.5 years old) answered the questionnaire. Within the last month, 66 (3%) students reported smoking. Most students (1599, 70%) had heard of e-cigarettes, with 380 (24%) learning from a store sign/display. E-cigarette use was reported by 238 (11%) students, with 134 (56%) having used it once and 18 (8%) reporting frequent/daily use. The majority (171, 72%) tried it because it was “cool/fun/new” while 14 (6%) used it to help smoke less or quit. Use was greater amongst males, those whose family or friends smoked, and in those with cigarette or other tobacco use (Table 1). Self-identified fair/poor health rating (OR 1.9 (95% CI 1.2–3.0, P<0.001)) and high stress level (OR 1.7 (95% CI 1.1–2.7, P<0.001)) were associated with increased odds of e-cigarette use. E-cigarette use was associated with lower average household incomes (33.4±8.4 vs. 36.1±10.7 × $1000, P=0.001). E-cigarette use is common amongst Canadian adolescents. Smoking reduction and cessation do not appear to be motivating factors regarding its use. E-cigarette use is associated with individual, family, and friend tobacco use, among other associations. These findings support the implementation of strict regulations to help reduce use amongst Canadian adolescents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".