The natural history of quitting smoking: findings from the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
AIMS: To describe the long-term natural history of a range of potential determinants of relapse from quitting smoking. DESIGN, SETTING AND PARTICIPANTS: A survey of 2502 ex-smokers of varying lengths of time quit recruited as part of the International Tobacco Control (ITC) Four Country Survey (Australia, Canada, United Kingdom, United States) across five annual waves of surveying. MEASUREMENTS: Quitters were interviewed by telephone at varying durations of abstinence, ranging from 1 to 1472 days (about 4 years) post-quitting. Smoking-related beliefs and experiences (i.e. urges to smoke; outcome expectancies of smoking and quitting; and abstinence self-efficacy) were included in the survey. FINDINGS: Most theorized determinants of relapse changed over time in a manner theoretically associated with reduced risk of relapse, except most notably the belief that smoking controls weight, which strengthened. Change in these determinants changed at different rates: from a rapidly asymptoting log function to a less rapidly asymptoting square-root function. CONCLUSIONS: Variation in patterns of change across time suggests that the relative importance of each factor to maintaining abstinence may similarly vary.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".