Editorial: Emerging research on social security and population health: new opportunities and challenges
Bibliographic record
Abstract
the emergence of new economic and social dynamics, such as the rise of the digital economy, the economic downturn caused by the COVID-19 pandemic, and the acceleration of population aging. These factors have introduced both opportunities and challenges to the interplay between social security and population health. For instance, advancements in digital medical technology have enhanced the efficiency and health outcomes of medical insurance operations, whereas the aging population poses health risks to the elderly and increases their healthcare expenses by potentially reducing pension benefits. The complexity of these environmental changes complicates the understanding of the relationship between social security and population health, necessitating further research for analysis. This special issue aims to gather original qualitative and/or quantitative research articles that deepen our comprehension of the relationship between social security and population health within the context of evolving social and economic conditions. The call for articles specifically focuses on exploring the opportunities and challenges presented by social security in influencing population health under new environmental circumstances. Additionally, contributions examining the connection between social security and population health from novel perspectives, including the underlying mechanisms and economic implications of social security's impact on population health, are encouraged. In sum, any original and significant research concerning social security and population health is of interest for this special issue.In this editorial, we provide a summary of the articles published in the Research 2023), the relationship between the digital economy and residents' health is explored using data from the China Family Panel Studies (CFPS) in 2020. Their findings suggest that the digital economy has significantly enhanced the overall health status of residents, particularly those residing in the eastern region. The positive impact of the digital economy on residents' health primarily stems from its promotion of regional green development. Their study reveals that URRBMI has a substantial positive impact on the physical health of rural older adults, particularly those in the eastern regions and those who are more advanced in age. In a separate study, Li et al. (2023) analyze data from the China Health and Retirement Longitudinal Study (CHARLS) spanning from 2011 to 2018, employing a staggered difference-in-differences model to evaluate the effects of integrating urban-rural health insurance on poverty vulnerability among rural residents. Their results indicate a significant reduction of 6.32% in poverty vulnerability due to the integration of urban-rural health insurance. Furthermore, the analysis of heterogeneity demonstrates that the integration of urban-rural medical insurance has a more pronounced impact on vulnerable groups with poorer health conditions than on those with better health, leading to a significant decrease in poverty vulnerability among individuals with chronic diseases.Fan and Hua (2023) investigate the spillover effects and influencing mechanisms of the new rural insurance policy on human capital investments in rural households using data from the China Family Panel Studies (CFPS) in 2010, 2012, 2014, 2016, and 2018. The findings indicate that participation in the New Cooperative Medical Scheme (NCMS) significantly boosts human capital investments in rural households.Additionally, the spillover effects of the new policy vary significantly based on the gender, insurance stage, and family income of the insured individuals. The new rural insurance policy influences human capital investments in rural households through intergenerational interactions, impacting both the material aspects such as economic support, housework, and child care, as well as the non-material aspects like pension awareness.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".