The impact of televised tobacco control advertising content on campaign recall: Evidence from the International Tobacco Control (ITC) United Kingdom Survey
Bibliographic record
Abstract
BACKGROUND: Although there is some evidence to support an association between exposure to televised tobacco control campaigns and recall among youth, little research has been conducted among adults. In addition, no previous work has directly compared the impact of different types of emotive campaign content. The present study examined the impact of increased exposure to tobacco control advertising with different types of emotive content on rates and durations of self-reported recall. METHODS: Data on recall of televised campaigns from 1,968 adult smokers residing in England through four waves of the International Tobacco Control (ITC) United Kingdom Survey from 2005 to 2009 were merged with estimates of per capita exposure to government-run televised tobacco control advertising (measured in GRPs, or Gross Rating Points), which were categorised as either "positive" or "negative" according to their emotional content. RESULTS: Increased overall campaign exposure was found to significantly increase probability of recall. For every additional 1,000 GRPs of per capita exposure to negative emotive campaigns in the six months prior to survey, there was a 41% increase in likelihood of recall (OR = 1.41, 95% CI: 1.24-1.61), while positive campaigns had no significant effect. Increased exposure to negative campaigns in both the 1-3 months and 4-6 month periods before survey was positively associated with recall. CONCLUSIONS: Increased per capita exposure to negative emotive campaigns had a greater effect on campaign recall than positive campaigns, and was positively associated with increased recall even when the exposure had occurred more than three months previously.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".