The Effectiveness of Anti-Smoking Campaigns over the Life-Cycle and the Role of Information
Bibliographic record
Abstract
Our study documents the effectiveness of anti-smoking campaigns on various age groups and attempts to shed some light on the mechanism by which community interventions operate and affect smokers. We re-examine evidence from a large scale National Cancer Institute community-wide intervention study entitled `The Community Intervention Trial for Smoking Cessation' (COMMIT). Our empirical results show that this intervention has differential effects on the quit rates of smokers. This variation has not been observed in the earlier literature on anti-smoking campaigns and was not noticed by previous studies using the COMMIT data. The quit rates in the intervention group are found to be significantly higher for individuals aged 30 to 37 and those aged 60 and up, but lower for those younger than 30. The various channels of the COMMIT study were developed to create an awareness and recognition that smoking is a public health problem, and to change the social acceptability of smoking. In light of the age variation uncovered, we argue that the public information channel may play a crucial role in affecting change. In particular, public awareness about the negative health consequences of smoking is likely to be responsible for the increased quits among older smokers in the treatment group.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".