“Quitting Smoking Will Benefit Your Health”: The Evolution of Clinician Messaging to Encourage Tobacco Cessation
Bibliographic record
Abstract
Illnesses that are caused by smoking remain as the world's leading cause of preventable death. Smoking and tobacco use constitute approximately 30% of all cancer-related deaths and nearly 90% of lung cancer-related deaths. Thus, improving smoking cessation interventions is crucial to reduce tobacco use and assist in minimizing the burden of cancer and other diseases in the United States. This review focuses on the existing research on framed messages to promote smoking cessation. Consistent with the tenets of prospect theory and recent meta-analysis, gain-framed messages emphasizing the benefits of quitting seem to be preferable when working with adult patients who smoke tobacco products. The evidence also suggests that moderators of treatment should guide framed statements made to patients. Meta-analyses have provided consistent moderators of treatment such as need for cognition, but future studies should further define the specific framed interventions that would be most helpful for subgroups of smokers. In conclusion, instead of using loss-framed statements like "Smoking will harm your health by causing problems like lung and other cancers, heart disease, and stroke," as a general rule, physicians should use gain-framed statements like "Quitting smoking will benefit your health by preventing problems like lung and other cancers, heart disease, and stroke."
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".