Protocol to study the role of alcohol consumption and alcohol control policy for economic development and health in membership countries of the Association of South East Asian Nations (ASEAN) 2000–2023: A modelling study
Bibliographic record
Abstract
Introduction Economic development leads to higher life expectancy, and in most countries is also associated with a higher level of alcohol consumption, as more people start to consume alcohol. As alcohol consumption is a major risk factor for many diseases, this reduces life expectancy, and in turn slows down economic development. However, alcohol control policies may reduce consumption and mitigate the detrimental effects of alcohol use on life expectancy and the economy. The objectives of this study are to quantify the interrelations between alcohol control policies, levels and patterns of consumption, life expectancy, and economic development for the ASEAN countries between 2000 and today. Methods and analysis Secondary data analysis of available data, using a cross-sectionally correlated and time-wise autoregressive model for the main conceptual model. In addition, the detrimental effects of alcohol consumption on economic development are estimated directly and indirectly via its effect on health indicators. Ethics and dissemination As a secondary analysis of publicly available data, this study did not require approval of a research ethics board. Its results will be disseminated via peer-reviewed publications, webinars and other forms available to decision-makers. Strengths and limitations of this study Selection of a region (ASEAN countries) which experienced high growth of economic wealth as measured by increases in per capita gross domestic product in purchasing power parity. Standardized comparable data from international agencies (World Bank, World Health Organization) Strong conceptual model Sufficient data points to conduct the cross-sectionally correlated and time-wise autoregressive model Potential practical implications on health policy.
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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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 0.018 |
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".