Mental Health Problems and Initiation of E-cigarette and Combustible Cigarette Use
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: During adolescence, mental health problems may increase the risk of initiating combustible cigarette use. However, it is unknown if this association extends to electronic cigarettes (e-cigarettes). We examined whether internalizing and externalizing problems were associated with initiation of e-cigarette, combustible cigarette, and dual-product use among adolescents. METHODS: Participants were drawn from the Population Assessment of Tobacco and Health Study, a nationally representative longitudinal study of US adolescents followed from 2013 to 2015. The study sample included 7702 adolescents aged 12 to 17 years who at baseline reported no lifetime use of tobacco products. We examined the respective associations between baseline internalizing and externalizing problems and initiating use of e-cigarettes, combustible cigarettes, or both at 1-year follow-up. RESULTS: Compared with adolescents with low externalizing problems, adolescents with high externalizing problems were significantly more likely to initiate use of e-cigarettes (adjusted relative risk ratio [aRRR] = 2.78; 95% confidence interval [CI]: 1.76-4.40), combustible cigarettes (aRRR = 5.59; 95% CI: 2.63-11.90), and both products (aRRR = 2.23; 95% CI: 1.15-4.31). Adolescents with high internalizing problems were at increased risk of initiating use of e-cigarettes (aRRR = 1.61; 95% CI: 1.12-2.33) but not combustible cigarettes or both products. CONCLUSIONS: Mental health problems are associated with increased risk for initiating e-cigarette, combustible cigarette, and dual-product use in adolescence. This association is more consistent for externalizing problems than internalizing problems. Addressing mental health problems could be a promising target for preventing initiation of nicotine- and/or tobacco-product use by adolescents.
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 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 | 0.001 | 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".