To what extent do smokers make spontaneous quit attempts and what are the implications for smoking cessation maintenance? Findings from the International Tobacco Control Four country survey
Bibliographic record
Abstract
AIM: To assess the extent to which quit attempts are spontaneous and to evaluate if this is a determinant of smoking cessation maintenance, with better control for memory effects. METHODS: We use data from 3,022 smokers who made quit attempts between Waves 4 and 5 and/or Waves 5 and 6 of the International Tobacco Control Four country survey. Outcomes (quitting for 6 months) were confirmed at the next wave for cases where the attempt began within the previous 6 months. We assessed the length of delay between the decision to quit and implementation and whether the attempt followed a "spur-of-the-moment" decision or some serious prior consideration. Outcomes were modeled using generalized estimating equations. RESULTS: Prior consideration of quitting was unrelated to the outcome, but there were complex relationships for the delay between choosing a quit day and implementation. Those who reported quitting on the day they decided and those who delayed for 1 week or more had comparable rates of 6-month abstinence. Delaying for 1-6 days was associated with a greater relapse rate than those who quit on the day, although this effect became nonsignificant in multivariate analyses. CONCLUSIONS: Quitting is on most smokers' minds regularly and most attempts are not preceded by a long lead in period following the decision to try. Neither prior consideration nor delay between the decision to quit and implementation was clearly related to outcomes. Previous findings of greater success for spontaneous quit attempts may be because they conflate setting a date in advance with planning and also perhaps some differential memory effects.
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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 | 0.004 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".