Predictors of smoking relapse by duration of abstinence: findings from the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
AIM: To explore predictors of smoking relapse and how predictors vary according to duration of abstinence. DESIGN, SETTING AND PARTICIPANTS: A longitudinal survey of 1296 ex-smokers recruited as part of the International Tobacco Control (ITC) Four Country Survey (Australia, Canada, United Kingdom and United States). Measurements Quitters were interviewed by telephone at varying durations of abstinence (from 1 day to approximately 3 years) and then followed-up approximately 1 year later. Theorized predictors of relapse (i.e. urges to smoke; outcome expectancies of smoking and quitting; and abstinence self-efficacy) and nicotine dependence were measured in the survey. FINDINGS: Relapse was associated with lower abstinence self-efficacy and a higher frequency of urges to smoke, but only after the first month or so of quitting. Both these measures mediated relationships between perceived benefits of smoking and relapse. Perceived costs of smoking and benefits of quitting were unrelated to relapse. CONCLUSIONS: Challenging perceived benefits of smoking may be an effective way to increase abstinence self-efficacy and reduce frequency of urges to smoke (particularly after the initial weeks of quitting), in order to reduce subsequent relapse risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".