Differentiating Patterns of Vaping, Alcohol, and Cannabis Use Among Early Adolescents
Bibliographic record
Abstract
The current study assessed whether risk factors associated with vaping were distinct from risk factors associated with other substance use (e.g., alcohol, cannabis) during adolescence. Participants ( N = 848, ages 10–16 years) completed a self-report survey to assess frequency and age of onset of substance use, risk perceptions of use, risk factors (depressive symptoms, sensation seeking, and parent-reported factors), and vape nicotine content. Groups were created to differentiate types of substance use, and frequency of substance use. Overall, adolescents who only vaped had lower depressive symptoms and indicated less nicotine vape use than adolescents who vaped and used other substances. Experimental vapers perceived other substances (but not vaping) as risky and endorsed vaping nicotine less often than regular vapers. Early initiation of vaping was not associated with polysubstance use. Taken together, our findings offer important implications for how vaping can be differentiated from other substance use during adolescence.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".