Predictors of past-year e-cigarette use among young adults
Bibliographic record
Abstract
Understanding the underpinnings of e-cigarette use among young adults is critical to addressing increasing uptake. We identified predictors of past-year e-cigarette use among young adults in Montreal, Canada. Data on potential predictors were available for 714 young adults participating in the ongoing Nicotine Dependence in Teens Study at age 20 in 2007-08. Past-year e-cigarette use was measured at age 30 in 2017-20. Each potential predictor was studied in a separate multivariable logistic regression model controlling for age, sex, and educational attainment. Male sex, friends who smoke, cigarette smoking, use of other tobacco products, alcohol use, use of marijuana, and impulsivity predicted past-year e-cigarette use. Higher educational attainment and very good/excellent self-rated health were protective. Program and policy makers will need to consider these predictors of e-cigarette use in the design of clinical and public health interventions targeting e-cigarette use in young adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".