Predictors of E-Cigarette Initiation: Findings From the Youth and Young Adult Panel Study
Bibliographic record
Abstract
OBJECTIVES: Although previous studies have identified reasons why youth try e-cigarettes, longitudinal research is needed to identify predictors of e-cigarette initiation. This study assesses predictors of e-cigarette initiation among youth and young adults in the 2018-2019 Youth and Young Adult Panel Study. METHODS: This study examined the proportion of Canadian participants aged 16 to 25 (n = 137) reporting never use of e-cigarettes at baseline in 2018. Individuals were categorized as not initiated and initiated at 12-month follow-up. We examined demographic characteristics, substance use, health status, social influences and perception by initiation category. Adjusted odds ratios (AORs) were calculated using logistic regression models and multivariable logistic regression model. RESULTS: Among the 137 never e-cigarette users at baseline, 59% remained never users while 41% initiated use of e-cigarettes during the 12-month follow-up. The results of multivariable logistic regression analysis showed that regularly seeing anyone use e-cigarettes (AOR: 4.11; 95% CI: 1.04, 16.31) and seeing anyone use e-cigarettes very often or always at baseline (AOR: 4.54; 95% CI: 1.21, 17.01) is associated with initiating e-cigarette use among youth and young adults. CONCLUSION: The results revealed social influences to be the most important predictors of initiation among youth and young adults. Interventions to prevent youth and young adults from initiating e-cigarette use should expand from only focusing on peer use to reducing use in public space such as parks and recreational facilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".