Social Marketing in Malaysia: Cognitive, Affective, and Normative Mediators of the TAK NAK Antismoking Advertising Campaign
Bibliographic record
Abstract
Antismoking mass media campaigns are known to be effective as part of comprehensive tobacco control programs in high-income countries, but such campaigns are relatively new in low- and middle-income countries and there is a need for strong evaluation studies from these regions. This study examines Malaysia's first national antismoking campaign, TAK NAK. The data are from the International Tobacco Control Malaysia Survey, which is an ongoing cohort survey of a nationally representative sample of adult smokers (18 years and older; N = 2,006). The outcome variable was quit intentions of adult smokers, and the authors assessed the extent to which quit intentions may have been strengthened by exposure to the antismoking campaign. The authors also tested whether the impact of the campaign on quit intentions was related to cognitive mechanisms (increasing thoughts about the harm of smoking), affective mechanisms (increasing fear from the campaign), and perceived social norms (increasing perceived social disapproval about smoking). Mediational regression analyses revealed that thoughts about the harm of smoking, fear arousal, and social norms against smoking mediated the relation between TAK NAK impact and quit intentions. Effective campaigns should prompt smokers to engage in both cognitive and affective processes and encourage consideration of social norms about smoking in their society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".