Understanding the role of cessation fatigue in smoking relapse: Findings from the International Tobacco Control Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
BACKGROUND AND AIM: Relapse risk among people who formerly smoke is influenced by task difficulty. Cessation fatigue (CF) may be a better predictor than measures such as reported strength of urges to smoke (SUTS) and abstinence self-efficacy (ASE). It may also be affected by quit length and use of other nicotine products. The current study investigated whether post-quitting CF predicts higher relapse risk, its predictive utility relative to ASE and SUTS and whether the CF-relapse prediction was moderated by time since quitting. DESIGN: Data drawn from longitudinal cohort surveys conducted between 2016 and 2022 of the International Tobacco Control Four Country Smoking and Vaping Survey. SETTING: Canada, the United States, England and Australia. PARTICIPANTS: People aged 18 + years who formerly smoked (n = 1914). MEASUREMENTS: Generalised estimating equations logistic regression models were used to test for associations and moderation. FINDINGS: In separate individual analyses, CF, ASE and SUTS were statistically significant independent relapse predictors; however, when analysed together, CF was the only statistically significant relapse predictor [moderate CF: odds ratio (OR) = 1.64, 95% confidence interval (CI) = 1.21-2.23, P = 0.002; high CF: OR = 1.81, 95% CI = 1.07-3.07, P = 0.027) on top of continuing main effects of vaping and time since quitting, but time since quitting was not a moderator. CONCLUSIONS: Cessation fatigue appears to predict smoking relapse risk better than other measures related to task difficulty and does so independently of vaping and time since quitting, which are both protective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".