Adolescents' Attention to Traditional and Graphic Tobacco Warning Labels: An Eye-Tracking Approach
Bibliographic record
Abstract
The objective of this study was determine if the inclusion of Canadian-style graphic images would improve the degree to which adolescents attend to, and subsequently are able to recall, novel warning messages in tobacco magazine advertising. Specifically, our goal was to determine if the inclusion of graphic images would (1) increase visual attention, as measured by eye movement patterns and fixation density, and (2) improve memory for tobacco advertisements among a group of 12 to 14 year olds in the western United States. Data were collected from 32 middle school students using a head-mounted eye-tracking device that recorded viewing time, scan path patterns, fixation locations, and dwell time. Participants viewed a series of 20 magazine advertisements that included five U.S. tobacco ads with traditional Surgeon General warning messages and five U.S. tobacco ads that had been modified to include non-traditional messages and Canadian-style graphic images. Following eye tracking, participants completed unaided- and aided-recall exercises. Overall, the participants spent equal amounts of time viewing the advertisements regardless of the type of warning message. However, the warning messages that included the graphic images generated higher levels of visual attention directed specifically toward the message, based on average dwell time and fixation frequency, and were more likely to be accurately recalled than the traditional warning messages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".