Which types of anti-smoking television advertisements work better in Taiwan?
Bibliographic record
Abstract
Research in high income countries suggests that anti-smoking television advertisements with emotionally evocative graphic messages or personal testimonials that depict serious consequences from smoking are the most effective. Research to determine the most effective smoking cessation messages for low- and middle-income countries is needed to inform campaign development in these countries. Fifty-four male Taiwanese smokers, aged 18-34, rated advertisements and participated in a focus group to evaluate eight antismoking television advertisements with contrasting messaging strategies. Participants individually evaluated advertisements, after which they participated in a semi-structured focus group discussion (10 groups, 2-9 smokers per group). One week after this session, participants were called to assess advertisement recall. Both quantitative and qualitative data indicated that highly emotional testimonial ads that featured a graphic portrayal of personal suffering from the consequences of smoking and visceral graphic ads were more effective. The ad on tobacco industry denormalization that focuses on the responsibility of the industry for smoking-related harms was considered ineffective because smokers perceived it as having little personal relevance. Humorous advertisements were evaluated as the least effective because they lacked strong emotional content linked to smoking consequences. Qualitative results suggest that advertisement characteristics are more important than the demographic characteristics of people featured in advertisements. Study findings provide preliminary evidence that testimonial ads that involve graphic and emotionally evocative portrayals of smoking-attributed diseases and visceral graphic ads may have the greater potential to motivate Taiwanese smokers to quit smoking.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".