One Size Does Not Fit All When it Comes to Smoking Cessation: Observations from the International Tobacco Control Policy Evaluation Project
Bibliographic record
Abstract
The global community, through the World Health Organization’s Framework Convention on Tobacco Control (FCTC), is seeking to develop Guidelines for the implementation of Article 14 of the Convention, which deals with support for smoking cessation. This development requires models of how best to develop infrastructure and measures to promote and support cessation around the world. This special issue of Nicotine & Tobacco Research provides some evidence from the International Tobacco Control (ITC) Policy Evaluation Project that is contributing to an increased understanding of the challenges associated with encouraging and supporting smoking cessation. The ITC project (of which we are all leaders) is a research collaborative of more than 80 tobacco control researchers across 20 countries of which data from 7 countries are featured in this supplement. This commentary discusses three areas where the research reported here makes a contribution: our understanding of dependence; the effects of socioeconomic factors on cessation; and the potential utility of support programs. But first, we describe the context for this research.
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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 | 0.267 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.013 |
| 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".