Exploring the Bi-Directional Association between Tobacco and E-Cigarette Use among Youth in Canada
Bibliographic record
Abstract
Research has demonstrated associations between e-cigarette use and tobacco use among youth. However, few studies have examined whether reciprocal relationships exist between e-cigarette and tobacco use. The objective of this study was to examine whether bi-directional associations exist between e-cigarette and tobacco use in a large longitudinal sample of Canadian youth. A longitudinal sample of secondary students (n = 6729) attending 87 schools in Ontario and Alberta, Canada, who completed the COMPASS student questionnaire across three waves (from 2014–2015 to 2016–2017) was identified. Using cross-lagged models, we explored bi-directional associations between current tobacco and e-cigarette use, adjusting for relevant covariates. Our findings showed that current e-cigarette use predicted subsequent tobacco use between Wave 1 (W1) and Wave 2 (W2) of the study (W1–2: OR = 1.54, 95% CI = 1.37–1.74). Similarly, current tobacco use predicted e-cigarette use during earlier waves of the study (W1–2: OR = 1.43, 95% CI = 1.30–1.58). However, these relationships dissipated in later waves, when tobacco use no longer predicted e-cigarette use (W2–3: OR = 1.07, 95% CI = 0.99–1.16). This study extends prior work that focused mainly on the association between e-cigarette and subsequent tobacco use. Specifically, our findings portray a more complex relationship, where e-cigarette use may influence and be influenced by tobacco use.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".