Huiping Zheng
Datos Biográficos
| ID | 6589060 |
|---|---|
| NOMBRE | Huiping Zheng |
| NOMBRES | Huiping |
| APELLIDO | Zheng |
| FIRMA | ZHENG H |
| AFILIACIONES | Renmin University of China |
| ORCID | 0009-0006-1463-1841 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2025 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
Decomposing Differences in Cohort Health Expectancy by Cause and Age With Longitudinal Data
Highlights We propose a new method to decompose cohort health expectancy by age and cause. We develop a new attribution method for longitudinal data. This method handles interval censoring, semicompeting risks, and time-dependent covariates. We derive explicit formulas for stepwise decomposition of cohort health expectancy. We provide an R package and Shiny app to support use of the proposed method
Dual Clocks, Triangulated Self
Unmarried professional women remain an underexplored group in scholarship on the work–family interface and gendered identities. This study, applying interpretative phenomenological analysis (IPA), examines how unmarried professional women in China navigate competing temporal regulations and construct selfhood in daily life through the framework “dual clocks, triangulated self”. Findings suggest that the participants mostly prioritize the linear a…
Sin obras prominentes en esta página.
Dual Clocks, Triangulated Self
Unmarried professional women remain an underexplored group in scholarship on the work–family interface and gendered identities. This study, applying interpretative phenomenological analysis (IPA), examines how unmarried professional women in China navigate competing temporal regulations and construct selfhood in daily life through the framework “dual clocks, triangulated self”. Findings suggest that the participants mostly prioritize the linear a…
Decomposing Differences in Cohort Health Expectancy by Cause and Age With Longitudinal Data
Highlights We propose a new method to decompose cohort health expectancy by age and cause. We develop a new attribution method for longitudinal data. This method handles interval censoring, semicompeting risks, and time-dependent covariates. We derive explicit formulas for stepwise decomposition of cohort health expectancy. We provide an R package and Shiny app to support use of the proposed method
Agency (philosophy (1 obras) · China (1 obras) · Cohort (1 obras) · Cohort study (1 obras) · Construct (python library (1 obras) · Dual (grammatical number (1 obras) · Expectancy theory (1 obras) · Gender Diversity and Inequality (1 obras) · Gender Roles and Identity Studies (1 obras) · Global Health Care Issues (1 obras)