Type of e-liquid vaped, poly-nicotine use and nicotine dependence symptoms in young adult e-cigarette users: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Data are lacking on type of e-liquid vaped among e-cigarette users. Further, few studies assess all sources of nicotine used by e-cigarette users to assess whether poly-nicotine use relates to nicotine dependence (ND). The objectives were to describe young adult e-cigarette users by: (i) type of e-liquid vaped; (ii) poly-nicotine use; (iii) ND symptoms; and (iv) attempts to quit conventional cigarettes. METHODS: Data were available in cycle 23 of a longitudinal investigation on the natural course of cigarette smoking and ND. A total of 775 young adults (44% male; mean (SD) age 30.5(1.0)) completed mailed self-report questionnaires in 2017-20. RESULTS: Of 775 participants, 149 (19.2%) reported past-year e-cigarette use. Overall, 55.0% of e-cigarette users had used cannabis-containing e-liquid (31.5% vaped cannabis e-liquid exclusively); 50.4% used nicotine-containing e-liquid (23.5% vaped nicotine e-liquid exclusively); and 39.9% used e-liquid without nicotine (8.7% vaped e-liquid without nicotine exclusively). Most e-cigarette users (82.6%) used other nicotine-containing products including conventional cigarettes (72.5%); 60.8% reported ND symptoms, rising to 79.4% among those who vaped nicotine-containing e-liquid. Finally, 29.0% tried to quit conventional cigarettes using e-cigarettes in the past-year, but only 16.7% found them helpful. CONCLUSIONS: E-cigarettes now appear to appeal to a broader market than smokers who want to quit. More than half of young adult e-cigarette users vaped cannabis-containing e-liquid in the past year while only one-quarter had used e-cigarettes to assist with cessation. Most e-cigarette users used multiple nicotine-containing substances (including combustible cigarettes) which were associated with reports of ND symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".