Does Self-Reported smoking cessation fatigue predict making quit attempts and sustained abstinence among adults who smoke Regularly?
Bibliographic record
Abstract
BACKGROUND: Quitting smoking is difficult and many people who smoke experience cessation fatigue (CF) as a result of multiple failed attempts. This study examined the association of CF with making and sustaining a smoking quit attempt. METHODS: Data analysed were 4,139 adults (aged 18 years or older) who smoked daily or weekly and participated in the 2018 and 2020 International Tobacco Control Four Country Smoking and Vaping Surveys (ITC 4CV) conducted in Australia, Canada, England, and the US. CF was assessed at baseline using a single question: "To what extent are you tired of trying to quit smoking?" with response options: "Not at all tired"; "Slightly tired"; "Moderately tired"; "Very tired"; or "Extremely tired". We used binary logistic regression models to test the hypothesis that baseline CF would predict lower odds of both making a quit attempt and sustaining abstinence for a month or longer at follow-up adjusted for socio-demographic and smoking/vaping-related covariates. RESULTS: Persons who currently smoked and reported at least some CF were more likely to make a quit attempt, but less likely to sustain abstinence for at least one month, than those who reported no CF. These associations were independent of socio-demographic variables, and they did not differ by country. CONCLUSION: Contrary to expectation, CF was positively associated with making a quit attempt and non-linearly associated with lower rates of sustained abstinence at follow-up. While these findings should be replicated, they suggest that people with CF may benefit from targeted support to remain abstinent after a quit attempt.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".