Gender, spousal caregiving, and depressive symptoms among Chinese older adults: does work status matter?

OBJECTIVES: This study aims to examine the impacts of spousal caregiving on caregivers' depressive symptoms, and how work status and gender mitigate the relationship. METHOD: We used four waves' data from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018, N = 20,213) with linear mixed-effect models to investigate the association between providing instrumental activities of daily living (IADL) and activities of daily living (ADL) assistance to a spouse and spouse caregivers' depressive symptoms. We further examined the moderating effect of work status. Analyses were stratified by gender. RESULTS: As main effects, respondents who provided ADL assistance to their spouse had significantly higher depressive symptoms than non-spousal caregivers, and the association was particularly stronger for women than for men. However, we did not find significant difference of depressive symptoms between IADL spousal caregiver and non-spousal caregivers. Working while ADL spousal care further exacerbated caregivers' depressive symptoms for both genders, whereas working full-time while providing IADL spousal care is only associated with elevated depressive symptoms for women. Interestingly, we found that providing IADL assistance is associated with lower depressive symptoms while they were not working. CONCLUSION: The relationship between spousal caregiving and depressive symptoms differed significantly by the type of care and was also moderated by work status, and female caregivers on average have worsened depressive symptoms than male caregivers. Future research on caregiving needs to consider the types of care and other social roles that caregivers also take on. Lastly, more affordable eldercare facilitates need to be built to alleviate the burden among spousal caregivers, especially when they are working.

Does school learning shape gender ideology? Academic performance and adolescents' attitudes toward gender practices.

Despite research linking education to values, our understanding of the effects of academic learning on gender attitudes is still limited. Using sibling data collected over time, we investigate how learning in school, measured by achievement test scores, affects adolescents' views on gender issues both with and without direct implications for women's economic mobility. With fixed-effects models accounting for unobserved heterogeneity between high and low achievers, we show that the relationship between academic achievement and gender ideology is not spurious, but learning does not enlighten adolescents on all gender-related beliefs, either. Rather, school learning socializes both boys and girls into more liberal views on issues clearly related to women's economic opportunities. For views concerning dating practices or boy-girl interactions, which are irrelevant to the meritocracy-based mainstream values, academic performance has less consistent effects, with higher achievement scores sometimes associated with more conservative views among boys. Our results generally support the socialization and reproduction model of the role of school learning, although self-interest also explains high and low achievers' different attitudes on dating and other personal-realm gender practices.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.