THE EFFECT OF INCOME SECURITY IN OLD AGE ON HEALTH OUTCOMES: A COMPARISON BETWEEN CHINA AND THE US
Bibliographic record
Abstract
Abstract This paper examines the social determinants of health, particularly the social, political, and economic context of socioeconomic status and health outcomes. Many studies on the political economy of health have highlighted the effects of welfare states on health and health inequalities, typically using welfare state regimes as proxies for social policies. Few research studies have described the association between frailty phenotype and financial wellbeing, however, particularly considering the three-legged stool of retirement income security (public pensions, private pensions, and personal savings/assets) across different welfare state regimes. This study investigates the effects of old-age income disparities on frailty among older adults in the United States and China. Specifically, this paper examines the associations between different sources of old-age income (public and private pensions, personal savings and assets, earnings, workplace subsidies, worker’s compensation, household subsidies, and unemployment insurance benefits) and the five-item frailty phenotype from the RAND Health and Retirement Study (HRS) and the China Health and Retirement Longitudinal Study (CHARLS). The findings reveal that health outcomes measured by the Fried’s frailty phenotype vary considerably by country and income type.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".