US young adults’ perceived effectiveness of draft pictorial e-cigarette warning labels
Bibliographic record
Abstract
SIGNIFICANCE: Research shows that pictorial warning labels for cigarettes are more effective than text-only warnings, and preliminary work suggests that pictorial warnings could also be considered for electronic cigarettes (e-cigarettes). Pictorial warnings may be important for maximising their effectiveness among young people and enhancing the salience of the single nicotine addiction warning required for e-cigarettes to date in the USA. This study collected pilot data about the perceived effectiveness of draft e-cigarette pictorial warnings. METHODS: Participants were 876 young adults (ages 18-29) recruited through Amazon Mechanical Turk who completed an online e-cigarette survey in 2018. Participants viewed and ranked five versions of the same e-cigarette nicotine addiction warning message-four pictorial and one text-only-on their perceived noticeability, likelihood of capturing young people's attention, memorability, relevance to the addiction warning text and overall effectiveness in warning people about e-cigarette risks. For each outcome, presentation of the five warning versions was randomised. Pictorials included symbolic images of risk and addiction, and of priority audiences for the warning (ie, young people). RESULTS: For all outcomes, pictorial warnings were ranked higher than the text-only warning, and the warning using a yellow triangle caution icon was ranked highest for all outcomes. The text-only warning was ranked as the least likely to be effective for all four outcomes in which it was assessed. Trends were similar for current e-cigarette users and non-users. CONCLUSIONS: Future research should assess perceptions and the appropriateness of pictorial imagery for e-cigarette warnings and test their efficacy against text-only warnings experimentally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".