The Quitting Rollercoaster: How Recent Quitting History Affects Future Cessation Outcomes (Data From the International Tobacco Control 4-Country Cohort Study)
Bibliographic record
Abstract
INTRODUCTION: Most smokers have a history of unsuccessful quit attempts. This study used data from 7 waves (2002-2009) of the International Tobacco Control 4-country cohort study to examine the role of smokers' quitting history (e.g., recency, length, and number of previous quit attempts) on their subsequent likelihood of making a quit attempt and achieving at least 6 months of sustained abstinence. METHODS: Generalized estimating equations were used, allowing for estimation of relationships between variables across repeated observations while controlling for correlations from multiple responses by the same individual (29,682 observations from 13,417 individuals). RESULTS: The likelihood of a future quit attempt increased independently with recency and number of prior attempts. By contrast, the likelihood of achieving sustained abstinence of at least 6 months was reduced for smokers with a failed quit attempt within the last year (15.1% vs. 27.1% for those without, p < .001). Two or more failed attempts (vs. only one) in the previous year were also associated with a lower likelihood of achieving sustained abstinence (OR: 0.57, 95% CI: 0.38-0.85). Effects persisted after controlling for levels of addiction, self-efficacy to quit, and use of stop-smoking medications. CONCLUSIONS: There appears to be a subset of smokers who repeatedly attempt but fail to remain abstinent from tobacco. Understanding why repeated attempts might be less successful in the long term is an important research priority because it implies a need to tailor treatment approaches for those who are motivated to quit but persistently relapse back to smoking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".