The Association Between Adolescent Vaping and Subsequent Use of Other Substances and Risk Factors for Polysubstance Use
Bibliographic record
Abstract
Abstract Objectives Adolescent vaping has become a public health concern. The aim of this study was to examine the associations between adolescent vaping and subsequent use of other substances and risk factors for polysubstance use. Methods The Well-being and Experiences Study is a longitudinal, intergenerational study conducted in Manitoba, Canada. The sample for this study consisted of adolescents and emerging adults who participated in Waves 1 (N = 1,002; 2017-18; aged 14–17 years) and 2 (n = 756; 2019; aged 15–20 years). Multinomial logistic regression analysis was performed. Results Adolescent vaping was associated with continued use of alcohol, cannabis, and cigarettes (adjusted relative risk ratio [aRRR] range: 12.92–19.39), new onset use of cannabis (aRRR: 6.04) and cigarettes (aRRR: 3.66), and concurrent and simultaneous polysubstance use (aRRR range: 3.14–24.25). Several risk factors were identified for concurrently using three or four substances in the past year (aRRR range: 1.76–2.86) and simultaneously using alcohol, cannabis, and nicotine (aRRR range: 1.99–3.11). Among those who reported vaping nicotine at Wave 2, 33.6% of males and 24.2% of females reported doing so as a coping mechanism. Conclusions Adolescent vaping is a risk factor for subsequent use of other substances and polysubstance use. Efforts are needed to prevent vaping initiation and help adolescents with cessation. Strategies should include selective interventions for those with histories of childhood adversity and mental health disorder.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".