How does a failed quit attempt among regular smokers affect their cigarette consumption? Findings from the International Tobacco Control Four-Country Survey (ITC-4)
Bibliographic record
Abstract
Recent cross-sectional data suggests that smokers tend to reduce smoking following a failed self-initiated quit attempt, possibly motivated by the need to reduce harms or to facilitate future quitting or both. This study prospectively examined changes in cigarette consumption among adult smokers who relapsed from a quit attempt. It uses data from the first three waves of the International Tobacco Control Four-Country Survey (ITC-4), a random digit-dialed telephone survey of a cohort of over 9,000 adult smokers from the United Kingdom, United States, Canada, and Australia, followed up annually. Compared with those who did not make a quit attempt, relapsers were more likely to reduce consumption (average reduction of 0.7 vs. 3.4, respectively) over a mean period of 7 months between waves 1 and 2. Of the relapsers, 52% reduced their consumption by 5% or more, but 22% increased it. Smokers who smoked heavily at baseline, whose last quit attempt ended more recently, was of longer duration, and quit via a gradual cut-down method were all independently associated with reducing smoking following a failed attempt. These findings were similar across all four countries and were successfully replicated using waves 2-3 data. Change in consumption between waves 1 and 2 (whether increase or decrease) was maintained by a substantial number a year later (wave 3), but change did not undermine nor promote quitting between waves 2 and 3.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".