Vaping Frequency in Young Users: The Role of Gender and Age Among Regular Users
Bibliographic record
Abstract
Background: Vaping is popular among adolescents and emerging adults; however, little is known about regular vaping patterns in older adolescents and emerging adults. Objective: The current study explored whether gender and age are associated with nicotine-based vaping frequency to ascertain ascertaining which subgroups of youth are most at-risk for frequent use. Methods: In a cross-sectional survey, participants using nicotine-based vaping devices (N=535, age range 16-24) reported frequency measured as vaping days in a week, number of episodes during which participants vaped, and puff numbers for each episode. A two-way multivariate analysis of variance was used to test the effects and interactions of gender and age groups on the three frequency outcomes. Results: Emerging adults and males vaped more frequently than their older adolescent and female counterparts, respectively. Specifically, emerging adults vaped more days per week in comparison to older adolescents, whereas males vaped more days per week and had more vaping episodes per day relative to females. Further, emerging adult males had more vaping episodes in comparison to other subgroups, and adolescent females as well as emerging adult males took more puffs per episode in comparison to emerging adult females. Conclusion: Vaping differences among subgroups suggests the need for policies to reduce regular nicotine vaping targeted toward specific gender and age groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".