Young Canadian e-Cigarette Users and the COVID-19 Pandemic: Examining Vaping Behaviors by Pandemic Onset and Gender
Bibliographic record
Abstract
The aim of this study was to test how youth and young adult e-cigarette users responded to the COVID-19 pandemic. The 2020 Youth and Young Adult Vaping Survey ( N = 1,308) included 540 (44.7%) participants that reported differences in their vaping behaviors since the onset of the pandemic. Gender was the only relevant covariate that yielded a significant effect and/or interaction through a multivariate test. A two-way multivariate analysis of variance was used to test the effect of pandemic onset (pre- vs. during-pandemic), gender (males vs. females), and their interaction on vaping behaviors (days of vaping per week, episodes of vaping per day, and puffs per vaping episode). Respondents reported fewer days of vaping per week, episodes of vaping per day, and puffs per vaping episode during-pandemic than pre-pandemic [ F (3,533) = 52.81, p &lt; 0.001, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow><mml:mrow><mml:mtext>p</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math> = 0.229]. The multivariate effect of gender on the three vaping outcomes was not statistically significant [ F (3, 533) = 2.14, p = 0.095, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow><mml:mrow><mml:mtext>p</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math> = 0.012], though the interaction between pandemic onset and gender was [ F (3, 533) = 2.86, p = 0.036, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:msubsup><mml:mrow><mml:mi>η</mml:mi></mml:mrow><mml:mrow><mml:mtext>p</mml:mtext></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math> = 0.016]. Males reported fewer episodes of vaping per day [ t (262) = 7.40, p &lt; 0.001, 95% CI: 5.19–8.97] and puffs per vaping episode [ t (263) = 3.23, p = 0.001, 95% CI:0.292–1.20] during-pandemic than pre-pandemic. Females reported fewer vaping episodes per day during-pandemic than pre-pandemic [ t (273) = 5.14, p &lt; 0.001, 95% CI: 2.76–6.18]. Further, females reported more frequent puffs per vaping episode in comparison to males during-pandemic [ t (538) = −2.38, p = 0.017, 95% CI: −2.09–0.200]. The COVID-19 pandemic presents an opportunity to reduce vaping through health promotion messaging. Since females take more puffs per vaping episode overall, they may benefit the most from greater vaping cessation supports.
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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) | 0.000 | 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 | 0.001 |
| 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".