Commercial Tobacco Endgame Goals: Early Experiences From Six Countries
Bibliographic record
Abstract
INTRODUCTION: Tobacco use is a major threat to health globally. A number of countries have adopted "endgame goals" to minimize smoking prevalence. The INSPIRED project aims to describe and compare the experiences of the first six countries to adopt an endgame goal. AIMS AND METHODS: Data were collected on the initial experiences of endgame goals in Canada, Finland, Ireland, New Zealand (Aotearoa), Scotland, and Sweden up to 2018. Information was collated on the nature of the endgame goals, associated interventions and strategies, potential enablers and barriers, and perceived advantages and disadvantages. RESULTS: The INSPIRED countries had relatively low smoking prevalences and moderate-to-strong smoke-free policies. Their endgame goals aimed for smoking prevalences of 5% or less. Target dates ranged from 2025 to 2035. Except for New Zealand (Aotearoa), all countries had an action plan to support their goal by 2018. However, none of the plans incorporated specific endgame measures. Lack of progress in reducing inequities was a key concern, despite the consideration of equity in all of the country's goals and/or action plans. Experience with endgame goals was generally positive; however, participants thought additional interventions would be required to equitably meet their endgame goal. CONCLUSIONS: There was variation in the nature and approach to endgame goals. This suggests that countries should consider adopting endgame goals and strategies to suit their social, cultural, and political contexts. The experiences of the INSPIRED countries suggest that further and more significant interventions will be required for the timely and equitable achievement of endgame goals. IMPLICATIONS: By 2018, six countries (Canada, Finland, Ireland, New Zealand (Aotearoa), Scotland, and Sweden) had introduced government-endorsed "endgame goals," to rapidly reduce smoking prevalence to very low levels by a specified date. The nature and implementation of endgame goals were variable. Early experiences with the goals were generally positive, but progress in reducing smoking prevalence was insufficient, particularly for priority groups. This finding suggests more significant interventions ("endgame interventions") and measures to reduce inequities need to be implemented to achieve endgame goals. Variation in the nature and experience of endgame goals demonstrates the importance of designing endgame strategies that suit distinct social, cultural, and political contexts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".