How much unsuccessful quitting activity is going on among adult smokers? Data from the International Tobacco Control Four Country cohort survey
Bibliographic record
Abstract
AIMS: To document accurately the amount of quitting, length of quit attempts and prevalence of plans and serious thought about quitting among smokers. DESIGN: We used longitudinal data from 7 waves of the International Tobacco Control Policy Evaluation Four Country Survey (ITC-4). We considered point-prevalence data and cumulative prevalence over the 7 years of the study. We also derived annual estimates of quit activity from reports of quit attempts starting only within more recent time-frames, to control for biased recall. SETTING: Australia, Canada, the United Kingdom and the United States. PARTICIPANTS: A total of 21,613 smokers recruited across seven waves. MEASUREMENTS: Reported life-time quit attempts, annual quit attempts, length of attempts, time since last attempt started, frequency of aborted attempts, plans to quit and serious thought about quitting. FINDINGS: Around 40.1% (95% CI: 39.6-40.6) of smokers report attempts to quit in a given year and report an average of 2.1 attempts. Based on free recall, this translates to an average annual quit attempt rate of 0.82 attempts per smoker. Estimates derived only from the preceding month to adjust for recall bias indicate an annual rate of approximately one attempt per smoker. There is a high prevalence of quit-related activity, with more than a third of smokers reporting thoughts or actions related to quitting in a given month. More than half the surveyed smokers eventually succeeded in quitting for at least 1 month, and a majority of these for over 6 months. CONCLUSIONS: Smokers think a great deal about stopping and make many unsuccessful quit attempts. Many have been able to last for extended periods and yet they still relapsed. More attention needs to be focused on translating quit-related activity into long-term abstinence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".