Smokers' responses toward cigarette pack warning labels in predicting quit intention, stage of change, and self-efficacy
Bibliographic record
Abstract
INTRODUCTION: This paper is concerned with the effects of cigarette pack warning labels on quitting intentions. We examined whether different responses among smokers toward cigarette pack warning labels could predict quit intentions and self-efficacy in quitting. Variables studied were "noticing warning labels during last month," "reading or looking closely at warning labels," "avoiding looking at labels during last month," "thinking about health risks of smoking because of the warning labels, "more likely to quit because of the warning labels," and "stopping from having a cigarette when about to smoke one because of the labels." METHODS: A total of 2,006 adult smokers in Malaysia were surveyed in face-to-face interviews using a standardized questionnaire. Of those, 1,919 male smokers were included in the analyses. RESULTS: The responses "more likely to quit because of the warning labels" and "stopped from having a cigarette when about to smoke one" significantly predicted all stages of change and self-efficacy, independent of the other measures. In addition, thinking about the health risks and reading the warnings more often added extra predictive capacity but only in the early stages of contemplating change. DISCUSSION: Less intense processing of the information may be important in initiating thoughts, but cognitions about quitting and foregoing cigarettes are the key mechanisms by which warnings stimulate quitting intentions and help smokers feel capable of succeeding. Malaysian smokers appear to respond to warnings in ways comparable with those from developed countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".