The Efficacy of Cigarette Warning Labels on Health Beliefs in the United States and Mexico
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Concern over health risks is the most common motivation for quitting smoking. Health warnings on tobacco packages are among the most prominent interventions to convey the health risks of smoking. Face-to-face surveys were conducted in Mexico (n = 1,072), and a web-based survey was conducted in the US (n = 1,449) to examine the efficacy of health warning labels on health beliefs. Respondents were randomly assigned to view two sets of health warnings (each with one text-only warning and 5-6 pictorial warnings) for two different health effects. Respondents were asked whether they believed smoking caused 12 different health effects. Overall, the findings indicate high levels of health knowledge in both countries for some health effects, although significant knowledge gaps remained; for example, less than half of respondents agreed that smoking causes impotence and less than one third agreed that smoking causes gangrene. Mexican respondents endorsed a greater number of correct beliefs about the health effects of smoking than did the U.S. sample. In both countries, viewing related health warning labels increased beliefs about the health risks of smoking, particularly for less well-known health effects such as gangrene, impotence, and stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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) | 0.000 | 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 it