Evaluación de las políticas contra el tabaquismo en países latinoamericanos en la era del Convenio Marco para el Control del Tabaco
Bibliographic record
Abstract
OBJECTIVE: The Framework Convention on Tobacco Control (FCTC) aims to coordinate tobacco control policies around the world that reduce tobacco consumption. The FCTC's recommended policies are likely to be effective in low- and middle-income countries. Nevertheless, policy evaluation studies are needed to determine policy impact and potential synergies across policies. MATERIAL AND METHODS: The International Tobacco Control Policy Evaluation Project (ITC) is an international collaboration to assess the psychosocial and behavioral impact of the FCTC's policies among adult smokers in nine countries. The ITC evaluation framework utilizes multiple country controls, a longitudinal design, and a theory-driven conceptual model to test hypotheses about the anticipated effects of given policies. RESULTS: ITC Project results generally confirm previous studies that form the evidence base for FCTC policy recommendations, in particular: the use of graphic warning labels; banning of "light" and "mild" descriptors; smoking bans; increasing tax and price; banning advertising; and using new cigarette product testing methods. CONCLUSIONS: Initial findings from the ITC Project suggest that Latin American countries could use similar methods to monitor and evaluate their own tobacco control policies while contributing to the evidence base for policy interventions in other countries.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".