Research Priorities for FCTC Articles 20, 21, and 22: Surveillance/Evaluation and Information Exchange
Bibliographic record
Abstract
INTRODUCTION: Framework Convention on Tobacco Control (FCTC) Articles 20, 21, and 22 call for strong monitoring and reporting of tobacco use and factors influencing use and disease (Articles 20 and 21) and for collaboration among the Parties and relevant organizations to share resources, knowledge, and expertise on all relevant tobacco control strategies (Article 22). METHODS: This paper provides background information and discusses research strategies that would strengthen these efforts and better inform the parties. By necessity, Articles 20 and 21 are discussed separately from Article 22, although 1 example that relates to both 20/21 and 22 is discussed at the end. RESULTS: Twelve important research opportunities on surveillance and evaluation are recognized, along with 4 on collaboration. The authors believe that the 6 most important areas for research would study (a) possible underreporting of tobacco use among certain demographic groups in some countries, (b) measures of industry activities, (c) optimal sampling strategies, (d) sentinel surveillance, (e) networks of tobacco companies and their partners as they promote tobacco use and interfere with implementation of the FCTC, and (f) network/relationship factors that impact diffusion of knowledge and decision making on the implementation of the FCTC. In addition, we call for a review process of existing surveillance and evaluation strategies to coordinate activities to make optimal use of existing resources. This activity would involve networking as prescribed in Article 22. CONCLUSIONS: Studies and activities such as these would facilitate control of the tobacco epidemic.
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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 | 0.354 | 0.383 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.039 | 0.021 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".