When pictures take away from the message: An examination of young adults’ attention to texting and driving advertisements.
Bibliographic record
Abstract
This study examined eye-movement patterns of young adults, while they were viewing texting and driving prevention advertisements, to determine which format attracts the most attention. As young adults are the most at risk for this public health issue, understanding which format is most successful at maintaining young adults' attention is especially important. Participants viewed nondriving, general distracted driving, and texting and driving advertisements. Each of these advertisement types were edited to contain text-only, image-only, and text and image content. Participants were told that they had unlimited time to view each advertisement, while their eye-movements were recorded throughout. Participants spent more time viewing the texting and driving advertisements than other types when they comprised text only. When examining differences in attention to the text and image portions of the advertisements, participants spent more time viewing the images than the text for the nondriving and general distracted driving advertisements. However, for texting and driving-specific advertisements the text-only format resulted in the most attention toward the advertisements. These results indicate that in attracting young adults' attention to texting and driving public health advertisements, the most successful format would be text-based. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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 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 | 0.000 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".