Challenges for building consensus for adoption and full compliance with 100% smoke free law in Serbia
Bibliographic record
Abstract
In Serbia, 37% of adults smoke and more than half of the population is exposed to tobacco smoke in enclosed places. The hospitality sector is exempted from the smoking ban and compliance with current legislation is low. From March 2017, Institute of Public Health of Serbia implemented a project aimed at building consensus for adoption and full compliance with 100% smoke free law in Serbia. The project was funded by Bloomberg Philanthropies and managed by the International Union Against Tuberculosis and Lung Disease. Project supported conferences and other events, contributed to the continuous presence in the media and built up informational resources to advocate for the smoke free environment which will be useful in the next period. New partnerships were established leading to an increase in a number of partners who will further work in tobacco control field. Based on the findings from surveys, conclusions from the multisectorial events and media content analysis, next steps and challenges were identified. In the next period, together with advocacy measures aimed at decision makers, it is important to focus on the general public and health professionals so they accept and advocate for evidence based tobacco control measures. Social norms changes are needed due to strong correlation between social norms and policy implementation. Moreover, it is necessary to conduct campaigns to change the perception of economic benefits of tobacco. New challenges such as heated tobacco products amplify the importance of the provision of accurate information on tobacco control best practice to decision makers, general public and health professionals. Findings from 2018 survey conducted as part of the project showed that only 26% of smokers and 64% of nonsmokers think smoking in the hospitality sector violates right to smoke free air, indicating the need of incorporating human rights approaches into further actions.
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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 | 0.076 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".