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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".