Review of Tobacco Taxes Advocacy in Indonesia: A Health Promotion Strategies
Bibliographic record
Abstract
Background: The global smoking prevalence from 2007 to 2021 decreased from 22.7% to 17%. However, in some countries, the prevalence has not changed or even increased. Indonesia is the third largest country in cigarette consumption. Data shows about 58 million male smokers and 3.5 million female smokers smoke every day. Many tobacco control efforts have been made, including efforts to increase cigarette excise taxes. In the process, there are advocacy efforts included in the health promotion strategy according to WHO in the Ottawa Charter. However, a complete review of the process and results of advocacy is still lacking, even though it can be used to evaluate the implementation of advocacy for future excise tax increases. Aims: This research aims to review the process and results of advocacy as a health promotion strategy in tobacco control. Methods: This research involves CISDI (Center for Indonesia’s Strategic Development Initiatives). The method used was Focus Group Discussion with CISDI and secondary data from political mapping in assessing advocacy results. Results: Advocacy of the excise tax increase policy carried out by CISDI received support from officials or the public amounting to 70.2% and only 23.6% disagreed. Conclusion: Health promotion strategies through advocacy can increase awareness and support from policy makers quite effectively. Tobacco control through increasing tobacco taxes can be carried out if all parties encourage the government to make policies. However, in reality, an increase in tobacco taxes alone cannot reduce cigarette consumption in the community.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".