Characteristics of Canadians who use vaping products, by smoking status: findings from the Canadian Community Health Survey, 2020
Bibliographic record
Abstract
INTRODUCTION: To date, surveillance of vaping among Canadians (using vaping products with or without nicotine) has largely been examined with respect to age and smoking status. However, a nationally representative examination of a broad set of characteristics is lacking. This study characterized Canadians aged 15 years and older who vape, stratified by smoking status. METHODS: Data from the 2020 Canadian Community Health Survey (unweighted analytical sample size: 28 413 respondents) were used to examine past-30-day vaping stratified by smoking status (current smoking, former smoking, and never/nonsmoking). A Sexand Gender-Based Analysis Plus approach was used to select individual-level characteristics for analysis. Descriptive statistics were used to examine outcomes by each characteristic and multivariable logistic regression models were constructed to identify significant factors associated with each past-30-day vaping by smoking status category, using weighted data. RESULTS: In 2020, 2.0% (605 000) of Canadians aged 15 years and older reported vaping and current smoking (dual use), 1.2% (372 000) reported vaping and former smoking and 1.1% (352 000) reported vaping and never/nonsmoking. Within each past-30-day vaping by smoking status category, certain subgroups presented higher risks: youth and young adults, men, and those having a mood and/or anxiety disorder had higher odds of dual use. Vaping and former smoking was associated with self-identification as a man, having a mood and/or anxiety disorder and provincial region. Youth and young adults, men and those identifying as not a visible minority had higher odds of vaping and never/nonsmoking. CONCLUSION: This analysis of Canadians who vape, stratified by smoking status, identifies high-prevalence subpopulations and informs us of the composition of vaping populations by select characteristics, deepening our understanding of Canadians who engage in vaping behaviours.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".