Weight control belief and its impact on the effectiveness of tobacco control policies on quit attempts: findings from the ITC 4 Country Survey
Bibliographic record
Abstract
BACKGROUND: Weight concerns are widely documented as one of the major barriers for girls and young adult women to quit smoking. Therefore, it is important to investigate whether smokers who have weight concerns respond to tobacco control policies differently than smokers who do not in terms of quit attempts, and how this difference varies by gender and country. OBJECTIVE: This study aims to investigate, by gender and country, whether smokers who believe that smoking helps control weight are less responsive to tobacco control policies with regards to quit attempts than those who do not. METHODS: We use longitudinal data from the International Tobacco Control Policy Evaluation Project in the USA, Canada, the UK and Australia to conduct the analysis. We first constructed a dichotomous indicator for smokers who have the weight control belief and then examined the disparity in policy responsiveness in terms of quit attempts by directly estimating the interaction terms of policies and the weight control belief indicator using generalised estimating equations. FINDINGS: We find that weight control belief significantly attenuates the policy impact of tobacco control measures on quit attempts among US female smokers and among UK smokers. This pattern was not found among smokers in Canada and Australia. CONCLUSIONS: Although our results vary by gender and country, the findings suggest that weight concerns do alter policy responsiveness in quit attempts in certain populations. Policy makers should take this into account and alleviate weight concerns to enhance the effectiveness of existing tobacco control policies on promoting quitting smoking.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".