Factors Associated with E-Cigarette Escalation among High School Students: A Review of the Literature
Bibliographic record
Abstract
BACKGROUND: E-cigarette use has been identified as a behaviour of concern among adolescents, and ever and daily use among this population has increased recently. The purpose of this review was to summarize the relevant studies investigating the frequency and intensity of e-cigarette use in adolescents and the factors associated with these patterns of use. METHODS: A scoping search of two databases was conducted to identify longitudinal studies examining escalating e-cigarette use among adolescents. Escalating e-cigarette use could refer to an increasing frequency or intensity of use over time. Articles were screened for relevance. Studies that met inclusion criteria were included for synthesis. RESULTS: Five articles were included for synthesis. All five articles were longitudinal studies taking place in the United States between 2013 and 2017. Age, gender, cost of e-cigarettes, use of cigarettes, polysubstance use, and e-liquid nicotine concentration were associated with escalation of e-cigarette use. CONCLUSIONS: A paucity of information exists regarding the escalation of e-cigarette use among adolescents. Given the changing popularity of devices, additional updated evidence is needed to understand the factors associated with the escalation of e-cigarette use among adolescents, which can be used to inform local and national programs and policies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".