Public support for cigarette price increase in Indonesia
Bibliographic record
Abstract
Objective The current governmental policies related to tobacco control in Indonesia are known to be not yet effective in reducing tobacco consumption. Therefore, increasing cigarette prices is one of effective instruments that should be supported by governments and public. This study aims to assess public support for cigarette price increase as well as to generate scientific evidence for the government and policymakers. Methods This research is a quantitative study with a cross-sectional design. The data were obtained through phone-based interviews in 1,000 respondents aged ≥ 18 years old in Indonesia. The interviews started on 1 May 2018 and ended on 31 May 2018. Results Respondents in this study were varied in terms of age, gender, level of education, income, occupation, the area of living, and smoking status. This study found that 88% of the respondents including 80% of smokers support cigarette price increase to prevent children from buying cigarettes. Around 66% smokers said they would stop smoking when the cigarette price is Rp60,000 (US$4.3) per package. The multivariate analysis revealed that age, income, cigarette expenditure per day, and perception on the current cigarette price are the predictors of support for cigarette price increase. Conclusions The increase in cigarette prices is supported by the majority of public including smokers. Cigarette price increase is supported by younger people and higher-income groups as they have less expenditure and less likely to smoke. Governments, academics, NGOs, and tobacco control activists should generate the understanding that increasing cigarette prices will improve the quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".