Cross-sectional and longitudinal associations between adolescent vaping and physical and mental health problems
Bibliographic record
Abstract
Purpose: Vaping among adolescents is concerning, given limited empirical evidence about effects on health. We examined associations between vaping and concurrent and new onset physical and mental health conditions in a sample of adolescents. Methods: Data were from Waves 1 to 3 of the longitudinal Well-Being and Experiences Study (n = 1002 aged 14-17 years at Wave 1) collected in Winnipeg, Manitoba (overall retention rate of 66.4 % at Wave 3). Past 30-day vaping was assessed at Wave 1. Health outcomes included both physical and mental health conditions. Data were analyzed using descriptive statistics and logistic regressions. Sex differences in associations were also examined. Results: At Wave 1, past 30-day vaping was reported by 27.8 % of the sample (28.1 % of males and 27.4 % of females); 42.1 % of adolescents reported having been diagnosed with at least one physical health condition, and 22.9 %, at least one mental health condition. Past 30-day vaping at Wave 1 was statistically significantly associated with concurrent mood disorders, alcohol/drug problems, and any mental health condition (odds ratios [ORs] = 1.77, 11.01, and 1.44, respectively) and new onset alcohol/drug problems and any mental disorder (ORs = 5.02 and 1.70, respectively) over the two-year follow-up period in unadjusted models. In fully adjusted models, only the association between vaping at Wave 1 and new onset alcohol/drug problems remained statistically significant (adjusted OR = 4.58). Associations was similar for males and females. Discussion: Vaping is common among adolescents. Providing youth with evidence-based data on potential harms might help them make informed decisions about vaping initiation, reduction, and cessation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".