Economic aspects of chronic diseases in Vietnam
Bibliographic record
Abstract
INTRODUCTION: There remains a lack of information on economic aspects of chronic diseases. This paper, by gathering available and relevant research findings, aims to report and discuss current evidence on economic aspects of chronic diseases in Vietnam. METHODS: DATA USED IN THIS PAPER WERE OBTAINED FROM VARIOUS INFORMATION SOURCES: international and national journal articles and studies, government documents and publications, web-based statistics and fact sheets. RESULTS: In Vietnam, chronic diseases were shown to be leading causes of deaths, accounting for 66% of all deaths in 2002. The burdens caused by chronic disease morbidity and risk factors are also substantial. Poorer people in Vietnam are more vulnerable to chronic diseases and their risk factors, other than being overweight. The estimated economic loss caused by chronic diseases for Vietnam in 2005 was about US$20 million (0.033% of annual national GDP). Chronic diseases were also shown to cause economic losses for families and individuals in Vietnam. Both population-wide and high-risk individual interventions against chronic disease were shown to be cost-effective in Vietnam. CONCLUSION: Given the evidence from this study, actions to prevent chronic diseases in Vietnam are clearly urgent. Further research findings are required to give greater insights into economic aspects of chronic diseases in Vietnam.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".