Chile’s Tobacco Strategy: A Scoping Review of Tobacco Measures Over Time
Bibliographic record
Abstract
Chile currently has a relatively robust tobacco control policy approach. Nonetheless, it has one of the highest tobacco consumption prevalence rates among adults worldwide. Better knowledge regarding changes in tobacco control policy over time may provide a comprehensive understanding of the observed changes in the prevalence of tobacco consumption. We aimed to carry out a scoping review to explore the evolution of Chile’s tobacco control consumption policies. We searched legal instruments, including laws and decrees, in the Chilean National Library of Congress from inception to December 2018. One author screened potential legal instruments; two authors independently systematized research records found. We included any legal instrument issued by the President of the Republic or the Chilean National Congress. We used the MPOWER strategy as a guideline to classify interventions. The institutional change theory proposed by van der Heijden and Kuhlmann was applied to interpret policy changes. We identified 499 legal instruments related to tobacco. Of these, three laws and ten decrees were included for analysis. We observed an uneven implementation of measures recommended by the WHO over time. The dimensions Protect, Warn, and Enforce showed consistent progress, measures in the Monitor and Raise dimensions were unsustained, and no measures for the dimension Offer were found. In Chile, measures to tackle tobacco consumption over the last three decades have focused predominantly on regulating individual choices rather than on the tobacco industry. Future research assessing the effects of Chile’s tobacco control consumption policies should account for their multifaceted and heterogeneous implementation.
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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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.035 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".