Smokers' sources of e-cigarette awareness and risk information
Bibliographic record
Abstract
INTRODUCTION: Few studies have explored sources of e-cigarette awareness and peoples' e-cigarette information needs, interests or behaviors. This study contributes to both domains of e-cigarette research. METHODS: Results are based on a 2014 e-cigarette focused survey of 519 current smokers from a nationally representative research panel. RESULTS: Smokers most frequently reported seeing e-cigarettes in stores (86.4%) and used in person (83%). Many (73%) had also heard about e-cigarettes from known users, broadcast media ads (68%), other (print, online) advertisements (71.5%), and/or from the news (60.9%); sources of awareness varied by e-cigarette experience. Most smokers (59.9%) believed e-cigarettes are less harmful than regular cigarettes, a belief attributed to "common sense" (76.4%), the news (39.2%) and advertisements (37.2%). However, 79.5% felt e-cigarette safety information was important. Over one-third said they would turn to a doctor first for e-cigarette safety information, though almost a quarter said they would turn to the Internet or product packaging first. Most (59.6%) ranked doctors as the most trustworthy risk source, and 6.8% had asked a health professional about e-cigarettes. CONCLUSIONS: Future research should explore the content of e-cigarette information sources, their potential impact, and ways they might be strengthened or changed through regulatory and/or educational efforts.
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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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".