Health Inequalities of Older People in China
Bibliographic record
Abstract
China is facing serious population aging issues because of unintended consequences of the economic reforms and social policies which began in the 1980s. Despite the remarkable progress in health status (e.g., the increasing life expectancy) in China, there remain large inequalities not only across the country, but also at a micro geographic scale. Health inequalities can be attributed to biological variations but also other variables including demographic socioeconomic and environmental indicators. Research on health inequalities in western countries is robust. However, there are few comprehensive studies of health inequalities in China. This research analyses the patterns of health inequalities and the associated social determinants of health with a specific focus on Chinese older people. Mixed methods are employed to garner a nuanced perspective on health inequalities and the social determinants of health. Data are extracted from both statistical surveys and semi-structure interviews. Quantitative methods are used to analyse the relationships between health inequalities and social determinants of health at various geographic scales. An analysis of semi-structured interviews is used to inform the quantitative finding providing further depth to an understanding of health inequalities among older people living in China. Several major findings result from this research. The population aging process has a significant influence on health inequalities for older people. China exhibits a significant rural-urban developmental divide with a noticeable impact on health inequalities. Socio-economic changes have significant impacts on health inequalities in China. The relationship between better socio-economic status and better health status is similar to but different than what is found in research in developed countries because of the various types of pensions. Compared to traditional environmental factors, the built environment in which older people live plays a significant role in determining health inequalities. To sum up, a comprehensive health care system is required in order to meet sustainable development and to narrow health inequalities in China. Socio-economic, environmental and other factors make significant contributions to determine older people’s health outcomes. As China is undergoing top-down social reform, health inequalities are widening. Promoting balance among different groups of older people is a principal challenge for Chinese society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".