Can Removing Tar Information From Cigarette Packages Reduce Smokers’ Misconceptions About Low-Tar Cigarettes? An Experiment From One of the World’s Lowest Tar Yield Markets, South Korea
Bibliographic record
Abstract
INTRODUCTION: Despite regulations that forbid cigarette packages from displaying messages such as "mild," "low-tar," and "light," many smokers still have misperceptions about "light" or "low-tar" cigarettes. One reason may be that tar amount displays continue to be permitted. This study examines whether removing tar delivery information from packaging reduces consumer misperceptions about "low-tar" cigarettes. METHODS: An online experiment was conducted in South Korea among 531 smokers who were randomly assigned to one of two conditions: with and without tar information on cigarette packages. Participants evaluated which type of cigarette was mildest, least harmful, easiest for nonsmokers to start smoking, and easiest for smokers to quit. RESULTS: Ten out of 12 chi-square tests showed that people judged the lowest reported tar delivery cigarette to be the mildest (p < .01), least harmful (p < .05), easiest to start (p < .05), and easiest to quit (p < .05)-less so in the "no-tar" condition than the "tar" condition. A higher level of misbeliefs about supposed low-tar cigarettes were found in the "tar" condition compared to the "no-tar" condition for all three brands (t = 5.85, 4.07, 3.82, respectively, p < .001). Regression analyses showed that the "no-tar" condition negatively predicted the level of misbeliefs after controlling for demographic and smoking-related variables (B [SE] = -.72 (.12), -.50 (.12), -.48 (.13), respectively, p < .001). CONCLUSIONS: Banning reported tar deliveries from cigarette packages is likely to reduce smokers' misconceptions about "low-tar" cigarettes. When reported tar deliveries are absent, smokers have inconsistent judgments about differently packaged cigarettes. IMPLICATIONS: When cigarette packages depict lower reported tar number deliveries, participants erroneously perceive them to be less harmful than packages displaying higher tar numbers. These misperceptions of harm may prompt smokers who might otherwise attempt to quit smoking to instead consume cigarettes with lower tar deliveries due to the mistaken belief that they will reduce their risk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".