Correlates of vaping among adolescents in Canada
Bibliographic record
Abstract
Background: Vaping is more prevalent among younger than older Canadians. While vaping is less harmful than combustible tobacco, it is not without health risk. Data and methods: Data from the 2019 Canadian Health Survey on Children and Youth were used to estimate vaping prevalence. Logistic regression models assessed the association of sociodemographic, youth, parenting and peer factors with vaping. The 2020 Canadian Community Health Survey identified adolescents who reported vaping before tobacco smoking. Data from the 2019 Canadian Tobacco and Nicotine Survey were used to examine vaping of e-liquids containing nicotine and flavours. Results: Vaping rates for 15- to 17-year-olds were nearly four times (21.3%) higher than those of 12- to 14-year-olds (5.4%). Two-thirds (66.1%) of 12- to 17-year-olds who had used both tobacco and e-cigarettes reported trying e-cigarettes first. E-liquids containing nicotine were used by 89.3% of 15- to 19-year-olds who reported vaping in the past 30 days; comparable with older adults. For both younger and older adolescents, having friends who engaged in negative behaviours, having been employed, and having consumed alcohol increased the odds. For 12- to 14-year-olds, attention deficit hyperactivity disorder was a risk factor, whereas having parents who usually knew who they were with and higher relatedness scores were protective. Among older adolescents, being male, being Canadian-born, having lower grades, and using tobacco or cannabis increased the odds of vaping. Interpretation: An adolescent's risk of vaping was most strongly correlated with other substance use, although other youth, parenting and peer characteristics also mattered. Because most of the data presented were collected before the COVID-19 pandemic and new vaping regulations, ongoing monitoring remains important.
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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 | 0.002 | 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".