Examining the correlates of cigarette smoking, e-cigarette use and dual use among Canadian post-secondary students
Bibliographic record
Abstract
Many Canadians use nicotine products such as cigarettes and e-cigarettes. A particular subpopulation of concern is post-secondary students given they have a higher prevalence of use. Many correlates of cigarette smoking and e-cigarette use have been identified. However, less focus has been on examining the correlates of cigarette smoking, e-cigarette use and dual use. This study explores the correlates of different nicotine modality use in post-secondary students. Using data from the Canadian Campus Wellbeing Survey (CCWS; n = 27,164), a multi-level nominal regression assessed the correlates of nicotine modality use. In comparison to individuals who were <20, individuals 20-24 (OR = .448, 95% CI .321, .625), 25-29 (OR = .140, 95% CI .093, .212), 30-34 (OR = .076, 95% CI .046, .125) and over 35 (OR = .041, 95% CI .024, .071) had lower odds of e-cigarette use compared to cigarette smoking. Identifying as a woman (OR = 1.553, 95% CI 1.202, 2.006), non-heterosexual (OR = .642, 95% CI = .485,0.851), current cannabis user (OR = 1.651, 95% CI 1.296, 2.104), and being an international student (OR = .350, 95% CI .251, .487) also impacted the odds of e-cigarette use vs only cigarette smoking. When considering dual use vs cigarette smoking, individuals aged 20-24 (OR = .491, 95% CI .337, .717), 25-29 (OR = .221, 95% CI .137, .357), 30-34 (OR = .163, 95% CI .091, .292) and over 35 (OR = .122, 95% CI .065, .230) had lower odds than individuals <20. Current cannabis use (OR = 1.680, 95% CI = 1.209, 2.138), binge drinking (OR = 1.885, 95% CI 1.384, 2.568), and international student status (OR = .689, 95% CI .476, .996) also impacted cigarette smoking vs dual-use. Overall, a minority of young adults (11.5%) at post-secondary institutions in our sample use nicotine products, and the higher prevalence of e-cigarette use warrants continued monitoring. Health promotion campaigns addressing e-cigarette use are required. Additionally, tailored intervention efforts could prioritize the treatment needs of international students studying in Canada.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".