The Association Between Adolescent Mental Health Symptoms and Characteristics of Cigarette and E-Cigarette Use
Bibliographic record
Abstract
INTRODUCTION: While there is a bi-directional relationship between internalizing mental health (IMH) symptoms (i.e., symptoms of depression, symptoms of anxiety) and nicotine use, it is unclear how IMH relates to patterns of use. The purpose of this study is to quantitatively examine the relationship between IMH symptoms and indicators of nicotine dependence, quit intentions, and quit attempts among adolescents who use cigarettes and/or e-cigarettes. METHODS: = 18,800). We measured indicators of nicotine dependence (i.e., frequency of use, time to first use, and perceived addiction), quit intention, and quit attempt history separately for cigarettes and e-cigarettes. Respondents reported current IMH symptoms (any vs. none). We examined associations between IMH symptoms and all outcomes using regression models, adjusting for dual use, and examining moderation by dual use. RESULTS: IMH symptoms were positively associated with e-cigarette nicotine dependence indicators and intention to quit e-cigarettes. IMH symptoms were positively associated with cigarette nicotine dependence indicators and intention to quit for those reporting dual use of e-cigarettes and cigarettes, but not exclusive cigarette use. IMH symptoms were positively associated with ever having a quit attempt for both products. CONCLUSIONS: Findings indicate that IMH symptoms were comorbid with greater dependence on nicotine products, particularly e-cigarettes. Smoking and e-cigarette cessation interventions should consider mental health, though research is needed to understand the directionality of the relationship between IMH symptoms and e-cigarette use.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".