Bibliographic record
Abstract
Background: Many countries have been legally prescribing\ngraphic health warning labels on cigarette\npackage as a part of their national policy of smoking\ncessation. This study was designed to evaluate the\neffectiveness and appropriateness of graphic health\nwarning labels of Canada, Singapore and European\nunion, in Korean for smoking cessation.\nMethods: From February to July in 2005, we surveyed 110\npeople who were high school students and college\nstudents. After showing them the 64 graphic health\nwarning labels (16 from Canada, 6 from Singapore and\n42 from European union), the self recorded questionnaires\nwere collected.\nResults: The effectiveness for smoking cessation was\nrelated to the arousal levels of visual effects and it had\nthe same result for each country in which we researched.\nThe high arousal loss-framed graphic health warning\nlabels were more effective than the gain-framed low\narousal ones.\nConclusion: To quit smoking, it can be reasonably\nconcluded that high and negative images of health that\nwere shown on warning labels of cigarette packages were\neffective. Hereafter, it is recommended more useful and\nproper designs of graphic warning labels be developed\nand applied. (J Korean Acad Fam Med 2007;28:923-930) Key words: graphic health warning, smoking cessation,\ntobacco pack
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".