The Role of the Subjective Importance of Smoking (SIMS) in Cessation and Abstinence
Bibliographic record
Abstract
INTRODUCTION: Each year about two thirds of U.S. smokers make a quit attempt. Yet, less than 5% remain abstinent three months post-quit date. One factor that may affect abstinence is negative feelings about the self-associated with being a smoker (disequilibrium), particularly if smoking is important to the sense of self and one is trying to quit. AIMS: We evaluated a multivariate structural equation model proposing that smoking's subjective importance to a smoker would influence carbon monoxide verified smoking abstinence at 24 weeks (post-quit date). Further, we assessed whether the relation would be moderated by the smoker's experience of disequilibrium. METHODS: Participants were 440 regular smokers taking part in a clinical trial assessing the effectiveness of different durations of nicotine replacement therapy use. Participants completed the subjective importance of smoking survey at baseline and were assessed for carbon monoxide verified seven-day point prevalence abstinence at 24 weeks. RESULTS: Using exploratory structural equation modelling, the subjective importance of smoking was associated with point prevalence abstinence at 24 weeks, but only for smokers with high disequilibrium. CONCLUSIONS: The results of this study suggest that experiencing negative feelings about being a smoker could motivate smokers to remain abstinent, despite the importance of smoking to the smoker's sense of self.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".