Predictive Power of Dependence Measures for Quitting Smoking. Findings From the 2016 to 2018 ITC Four Country Smoking and Vaping Surveys
Bibliographic record
Abstract
INTRODUCTION: To test whether urges to smoke and perceived addiction to smoking have independent predictive value for quit attempts and short-term quit success over and above the Heaviness of Smoking Index (HSI). AIMS AND METHODS: Data were from the International Tobacco Control Four Country Smoking and Vaping Wave 1 (2016) and Wave 2 (2018) surveys. About 3661 daily smokers (daily vapers excluded) provided data in both waves. A series of multivariable logistic regression models assessed the association of each dependence measure on odds of making a quit attempt and at least 1-month smoking abstinence. RESULTS: Of the 3661 participants, 1594 (43.5%) reported a quit attempt. Of those who reported a quit attempt, 546 (34.9%) reported short-term quit success. Fully adjusted models showed that making quit attempts was associated with lower HSI (adjusted odds ratio [aOR] = 0.81, 95% confidence interval [CI] = 0.73 to 0.90, p < .001), stronger urges to smoke (aOR = 1.08, 95% CI = 1.04 to 1.20, p = .002), and higher perceived addiction to smoking (aOR = 0.52, 95% CI = 0.32 to 0.84, p = .008). Lower HSI (aOR = 0.57, 95% CI = 0.40 to 0.87, p < .001), weaker urges to smoke (aOR = 0.85, 95% CI = 0.76 to 0.95, p = .006), and lower perceived addiction to smoking (aOR = 0.55, 95% CI = 0.32 to 0.91, p = .021) were associated with greater odds of short-term quit success. In both cases, overall R2 was around 0.5. CONCLUSIONS: The two additional dependence measures were complementary to HSI adding explanatory power to smoking cessation models, but variance explained remains small. IMPLICATIONS: Strength of urges to smoke and perceived addiction to smoking may significantly improve prediction of cessation attempts and short-term quit success over and above routinely assessed demographic variables and the HSI. Stratification of analyses by age group is recommended because the relationship between dependence measures and outcomes differs significantly for younger (aged 18-39) compared to older (aged older than 40) participants. Even with the addition of these extra measures of dependence, the overall variance explained in predicting smoking cessation outcomes remains very low. These measures can only be thought of as assessing some aspects of dependence. Current understanding of the factors that ultimately determine quit success remains limited.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".