Trends in youth e-cigarette and cigarette use between 2013 and 2019: insights from repeat cross-sectional data from the COMPASS study
Bibliographic record
Abstract
OBJECTIVES: E-cigarettes are an increasingly popular product among youth in Canada. However, there is a lack of long-term data presenting trends in use. As such, the objective of this study was to examine trends in e-cigarette and cigarette use across various demographic characteristics between 2013 and 2019 among a large sample of secondary school youth in Canada. METHODS: Using repeat cross-sectional data from a non-probability sample of students in grades 9 to 12, this study explored trends in the prevalence of ever and current e-cigarette use and cigarette smoking between 2013-2014 and 2018-2019 in British Columbia, Alberta, Ontario, and Quebec. Trends in ever and current e-cigarette use and cigarette smoking were studied across demographic variables among students in Ontario. RESULTS: The prevalence of e-cigarette ever and current use was variable across province and increased over time, particularly between 2016-2017 and 2018-2019. In contrast, the prevalence of current cigarette smoking was relatively stable over the study period, decreasing significantly in Alberta and Ontario between 2017-2018 and 2018-2019. In Ontario, the prevalence of ever and current e-cigarette use increased among all grades, both genders, and all ethnicities. CONCLUSION: Consistent with data from the United States, the prevalence of e-cigarette use among our large sample of Canadian youth has increased substantially in a short period of time. Surveillance systems should continue to monitor the prevalence of tobacco use among youth. Additional interventions may be necessary to curb e-cigarette use among Canadian youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".