Bibliographic record
Abstract
This article's objective is to investigate the impact of level of threat and layout of the picture on evoked fear and intention to quit smoking.A between-subject full-factorial 2 × 2 experimental design was constructed (level of threat × layout of picture).A total of 316 university students participated.A MANCOVA analysis was used.The intention to quit smoking is positively influenced by perceived severity.Perceived severity and self-efficacy, when the picture is in right and text is in left are more than when the picture is in left and text in right.Evoked-fear is more when the picture is in right and text in left.These findings are interesting as they open the door to create more effective threat-appeal messages by controlling or influencing layout of picture of the threat appeals.The main social implication of this article is for governments and (health) practitioners who are working against health risk among people.When researchers do better works in discovering effectiveness of elements, governments can benefit from this information.This research is focused on impact of elements such as layout of picture on threat appeals.This could be used to create more effective threat appeals.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.933 | 0.873 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".