Editorial: Emerging research on social security and population health: new opportunities and challenges
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,012 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».