Perceived Addiction as a Predictor of Smoking Cessation Among Occasional Smokers
Bibliographic record
Abstract
Introduction: Common short screening measures of dependence that use number of cigarettes per day may not be appropriate for use in populations of occasional smokers. Aims: In this study, we investigate whether perceived addiction (PA) predicts quit attempts and successful cessation among occasional smokers. Methods: Current occasional smokers (18+) in the Ontario Tobacco Survey (OTS) longitudinal cohort study followed up every six months for up to three years. Respondents rated their self-perceived level of addiction (very vs. somewhat or not very addicted). Generalised Estimating Equation models and proportional hazard models were used to test the predictive ability of PA. Results/Findings: Occasional smokers with very high PA had a higher likelihood of reporting a quit attempt (RR: 2.49; 95% CI: 1.88, 3.30) after adjusting for demographics. Given an incident quit attempt, occasional smokers who reported being very addicted were 2.93 times more likely to relapse (95%: 2.01, 4.28). The effect of PA was independent of other predictors of smoking behaviour. Conclusions: For some, occasional smokers, smoking cessation is a difficult process that may require significant support. Asking occasional smokers about PA is an effective way to predict likely success in quitting smokers that may be easily assessed in population based, as well as in community and clinical, settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".