The predictive utility of micro indicators of concern about smoking: findings from the International Tobacco Control Four Country study
Bibliographic record
Abstract
This study explored the association between six "micro indicators" of concern about smoking (1. stubbing out cigarettes before finishing; 2. forgoing cigarettes due to packet warning labels; thinking about... 3. the harms to oneself of smoking; 4. the harms to others of one's smoking; 5. the bad conduct of tobacco companies; and 6. money spent on cigarettes) and cessation outcomes (making quit attempts, and achieving at least six months of sustained abstinence) among adult smokers from Australia, Canada, the United Kingdom, and the United States of America. Participants were 12,049 individuals from five survey waves of the International Tobacco Control Four Country Survey (interviewed between 2002 and 2006, and followed-up approximately one year later). Generalized estimating equation logistic regression analysis was used, enabling us to control for within-participant correlations due to possible multiple responses by the same individual over different survey waves. The frequency of micro indicators predicted making quit attempts, with premature stubbing out, forgoing, and thinking about the harms to oneself of smoking being particularly strong predictors. An interaction effect with expressed intention to quit was observed, such that stubbing out and thinking about the harms on oneself predicted quit attempts more strongly among smokers with no expressed plans to quit. In contrast, there was a negative association between some micro indicators and sustained abstinence, with more frequent stubbing out, forgoing, and thinking about money spent on cigarettes associated with a reduced likelihood of subsequently achieving sustained abstinence. In countries with long-established tobacco control programs, micro indicators index both high motivation by smokers to do something about their smoking at least partly independent of espoused intention and, especially those indicators not part of a direct pathway to quitting, reduced capacity to quit successfully.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".