Evaluation of Graphic Cigarette Warning Images on Cravings to Smoke
Bibliographic record
Abstract
Abstract While health warnings are present on cigarette packs around the world, the nature of the warnings varies considerably between countries. In the United States, a small text warning citing the dangers of cigarette smoking is found on the side of all packs. This pilot study sought to determine whether graphic cigarette warning images, like those found in the United Kingdom and Canada, were better at decreasing cravings to smoke than existing text warnings found on cigarette packs in the United States. Twenty-five smokers seeking treatment to quit at a specialty tobacco treatment program were administered the Brief Questionnaire of Smoking Urges (QSU — BRIEF), a validated measure of craving, prior to and following exposure to cigarette pack warning images. The graphic cigarette warning images reduced cravings to smoke (6.20 point decrease) more than neutral images (3.36 point decrease) and current text warnings used in the United States (5.75 point decrease), although this difference was not statistically significant. Based on these pilot data, a larger study could further examine the effectiveness of graphic warning images and whether such warnings hold an advantage over the currently used text warnings.
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.
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 | 0.003 |
| 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 | 0.000 |
| 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".