Epidemiological Transition and Population Health: Understanding Social Determinants of Health in China
Bibliographic record
Abstract
This article has twin objectives: First, the article briefly examines major theoretical interpretations of disease causations in Western medicine, their limitations in understanding social epidemiology, and the gradual development of the population health approach to health promotion and disease prevention in the context of chronic diseases in Western industrialized societies. Second, the article examines the current epidemiological trends in China and the relevance of population health perspectives and strategies to promote health. While analyzing some recent findings on social determinants of health in China, the article argues that effective population health strategies for health promotion must be based on a social epidemiology that provides information necessary to promote health. Although infectious diseases still make a significant contribution to China’s mortality and morbidity figures, the incidence of chronic diseases such as malignancies, heart disease, respiratory disease, and cerebrovascular disease is steadily increasing. Finally, in view of the current epidemiological trend, and the need to tackle the multiple health challenges, this discursive analysis proposes a number of key research areas within the broader context of social epidemiology that may facilitate future health policies in China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".