E-Cigarette Flavors and Frequency of E-Cigarette Use among Adult Dual Users Who Attempt to Quit Cigarette Smoking in the United States: Longitudinal Findings from the PATH Study 2015/16–2016/17
Bibliographic record
Abstract
Potential mechanisms by which e-cigarette use may relate to combustible cigarette smoking cessation are not well-understood. We used U.S. nationally representative data to prospectively evaluate the relationship between e-cigarette flavor use and frequency of e-cigarette use among adult cigarette/e-cigarette dual users who attempted to quit smoking cigarettes. Analyses used Population Assessment of Tobacco and Health (PATH) Study data from adult dual users (2015/16) who attempted to quit smoking between 2015/16 and 2016/17 (Wave 3-Wave 4, n = 685, including those who did/did not quit by 2016/17). E-cigarette flavor use (usual/last flavor, past 30-day flavor; assessed in 2015/16) was categorized into Only tobacco; Only menthol/mint; Only non-tobacco, non-menthol/mint; and Any combination of tobacco, menthol/mint, other flavor(s). The key outcome, evaluated at follow-up in 2016/17, was frequent e-cigarette use, which was defined as use on 20+ of past 30 days. Logistic regression was used to evaluate associations between e-cigarette flavor use in 2015/16 and frequent e-cigarette use at follow-up in 2016/17. Dual users who attempted to quit smoking had greater odds of frequent e-cigarette use at follow-up when they used only non-tobacco, non-menthol/mint flavor than when they used only tobacco flavor as their regular/last e-cigarette flavor (OR = 1.9, 95% CI: 1.1–3.4); findings were no longer significant when adjusted for factors including e-cigarette device type (AOR = 1.4, 95% CI: 0.7–2.8). Past 30-day e-cigarette flavor use results were generally similar, although frequent e-cigarette use at follow-up was highest among those who used any combination of tobacco, menthol/mint, or other flavors. Findings indicate that e-cigarette flavor use among dual users who attempt to quit smoking may be related to e-cigarette use frequency overall, which may indicate a mechanism underlying findings for e-cigarette use and smoking cessation. Further longitudinal research may help to disentangle how e-cigarette characteristics uniquely impact e-cigarette use frequency and smoking cessation/sustained 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".