Heterogeneity in preferences for smoking cessation
Bibliographic record
Abstract
Promoting cessation is a cornerstone of tobacco control efforts by public-health agencies. Economic information to support cessation programs has generally emphasized cost-effectiveness or the impact of cigarette pricing and smoking restrictions on quit rates. In contrast, this study provides empirical estimates of smoker preferences for increased efficacy and other attributes of smoking cessation therapies (SCTs). Choice data were collected through a national survey of Canadian smokers. We find systematic preference heterogeneity for therapy types and SCT attributes between light and heavy smokers, as well as random heterogeneity using random parameters logit models. Preference heterogeneity is greatest between length of use and types of SCTs. We estimate that light smokers would be willing to pay nearly $500 ($CAN) to increase success rates to 40% with the comparable figure for heavy smokers being near $300 ($CAN). Results from this study can be used to inform research and development for smoking cessation products and programs and suggest important areas of future inquiry regarding heterogeneity of smoker preferences and preferences for other health programs.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".