Investigating individual-level correlates of e-cigarette initiation among a large sample of Canadian high school students
Bibliographic record
Abstract
INTRODUCTION: Having a better understanding of individual factors associated with e-cigarette initiation can help improve prevention efforts. Therefore, this study aimed to (1) identify baseline characteristics associated with e-cigarette initiation, and (2) determine whether changes in these baseline characteristics were associated with e-cigarette initiation. METHODS: This study used data from Year 6 (2017/18, baseline) and Year 7 (2018/19, follow-up) of the COMPASS study. The final sample included 12 315 students in Grades 9 to 11 who reported never having tried e-cigarettes at baseline. Students reported demographic information, other substance use, school behaviours, physical activity, sedentary behaviour, sleep, symptoms of anxiety and depression, and emotional regulation and flourishing scores. Hierarchical GEE models, stratified by gender, examined the association between (1) baseline characteristics and e-cigarette initiation at follow-up and (2) changes in baseline characteristics and e-cigarette initiation at follow-up. RESULTS: In total, 29% of students who had not yet initiated e-cigarette use reported initiating e-cigarette use at follow-up. Students in Grades 10 and 11 were less likely to initiate e-cigarette use. Other substance use, skipping school and meeting the physical activity guidelines at baseline and one-year changes to these behaviours were associated with e-cigarette initiation among both male and female students. Additionally, some differences were noted between females and males. CONCLUSION: Given that other health behaviours were associated with e-cigarette initiation, prevention approaches should target multiple health-risk behaviours to help prevent youth e-cigarette use. Additionally, school-based approaches may benefit by being implemented at the beginning of high school or in junior high school.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".