The near-universal experience of regret among smokers in four countries: Findings from the International Tobacco Control Policy Evaluation Survey
Bibliographic record
Abstract
Regret may be a key variable in understanding the experience of smokers, the vast majority of whom continue to smoke while desiring to quit. We present data from the baseline wave (October-December 2002) of the International Tobacco Control Policy Evaluation Survey, a random-digit-dialed telephone survey of a cohort of over 8,000 adult smokers across four countries--Canada, the United States, the United Kingdom, and Australia--to estimate the prevalence of regret and to identify its predictors. The proportion of smokers who agreed or agreed strongly with the statement "If you had to do it over again, you would not have started smoking" was extremely high--about 90%--and nearly identical across the four countries. Regret was more likely to be experienced by older smokers, women, those who had tried to quit more often, those who perceived quitting as conferring benefits, those with higher levels of perceived addiction, those who worried about future damage to health, those who perceived smoking as lowering their quality of life, those who perceived higher monetary costs of smoking, and those who believed that smoking is not socially acceptable. This predictive model was the same in all four countries. Regret is thus a near-universal experience among smokers in all four countries, and the factors that predict regret are universal across these four countries. Among other implications for cessation treatment and smoking prevention, this near universality of regret casts doubt on the view of some policy analysts and economists that the decisions to take up and continue smoking are welfare-maximizing for the consumer.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".