Awareness and use of the ‘Swap to Stop’ initiative among people who smoke or recently quit in England: findings from the 2024 International Tobacco Control Policy Evaluation Project (ITC) 4 Country Smoking and Vaping survey
Bibliographic record
Abstract
Tobacco smoking remains one of the leading causes of preventable morbidity, mortality and health inequalities in England. Efforts to reduce smoking, particularly among socioeconomically disadvantaged populations, require innovative approaches for England to achieve its Smokefree target, defined as reducing smoking rates to below 5% of the adult population (1, 2). Nicotine vaping is one of the most common smoking cessation aid among people who attempt to quit smoking in England, and research evidence supports use of vaping products as effective means for stopping smoking (3, 4). England is the first country in the world to implement a ‘Swap to Stop’ policy initiative in which the government has committed to providing free vapes to people who smoke alongside the offer of behavioural support (5). Swap to Stop is designed to reach priority populations, including individuals from lower sociodemographic backgrounds, those with mental or long-term physical health conditions, individuals in routine or manual occupations, and those for whom conventional smoking cessation methods have been ineffective. Reaching these groups is a critical step in ensuring the continuing decline in smoking prevalence across socioeconomic groups. Therefore, this study will examine factors associated with awareness of, access to, and participation in the Swap to Stop programme and with vaping harm perceptions among people who currently smoke or recently quit smoking. Funding: This study is funded by the NIHR (NIHR Unique Identifier = NIHR206123). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The International Tobacco Control Evaluation Survey is funded by: US National Cancer Institute P01CA200512; Canadian Institutes of Health Research FDN-148477; National Health and Medical Research Council of Australia APP1106451 and GTN1198301. Additional support to GTF is provided by a Senior Investigator Grant from the Ontario Institute for Cancer Research. References: 1. BALOGUN B. The smokefree 2030 ambition for England, House of Commons Library; 2023. 2. KHAN J. Making smoking obsolete. In: Office for Health Improvement and Disparities, editor; 2022. 3. KHOUJA J. N., TAYLOR A. E., MUNAFÒ M. R. Associations between reasons for vaping and current vaping and smoking status: Evidence from a UK based cohort, Drug Alcohol Depend 2020: 217: 108362. 4. MCDERMOTT M. S., EAST K. A., BROSE L. S., MCNEILL A., HITCHMAN S. C., PARTOS T. R. The effectiveness of using e-cigarettes for quitting smoking compared to other cessation methods among adults in the United Kingdom, Addiction 2021: 116: 2825-2836. 5. DEPARTMENT OF HEALTH AND SOCIAL CARE. Smokers urged to swap cigarettes for vapes in world first scheme; 2023.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".