Income Disparity and Mental Wellbeing among Adults in Semi-Urban and Rural Areas in Malaysia: The Mediating Role of Social Capital

The effect of adaptive capacity on resilience to the COVID-19 pandemic: A cross-country analysis

The COVID-19 pandemic's profound impacts on global health, driven by preparedness gaps and systemic risks, underscore the need to enhance societies' ability to manage both predictable risks and uncertainties inherent in disasters. While disaster research emphasises risk management for predictable threats and adaptive capacity for unexpected challenges, there is a lack of empirical examination of the impact of adaptive capacity on disaster resilience. This study addresses this gap by identifying three key adaptive capacities - quality of institutions, collaborative governance, and social capital - and examining their effects on COVID-19 resilience outcomes, measured by the ability to reduce excess mortality. Analysing secondary data from 129 nations using partial least squares structural equation modelling, the research finds significant positive effects of institutional quality and social capital on resilience outcomes. Conversely, collaborative governance shows a significant negative association, suggesting potentially intricate impacts beyond initial expectations. The findings highlight the need to enhance institutional quality and social capital to address preparedness gaps and unexpected challenges posed by biological hazards such as COVID-19. Future research should explore collaborative governance using a disaggregated approach that considers the roles of different stakeholders in various disaster phases.

Contribution

This study advances disaster research by presenting practical methodologies for operationalising adaptive capacities and empirically examining their effects on disaster resilience. For practitioners and policymakers, it highlights the need to adopt a long-term perspective in building disaster resilience, focussing on improving institutional quality and social capital to manage the uncertainties and complexities inherent in disaster scenarios effectively.

The prevalence of anxiety and its key influencing factors among the elderly in China

INTRODUCTION

With the rapid aging population, the mental health of older adults is paid more and more attention. Anxiety is a common mental health illness in older adults. Therefore, the study aimed to explore the current situation of anxiety and its factors among the elderly in China.

METHODS

Based on the data from 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), a total of 10,982 respondents aged 60 and above were selected. Generalized Anxiety Disorder (GAD-7) scale was used to assess the anxiety. Univariate and multivariate analysis were used to analyze the influencing factors of anxiety. Random forest was established to rank the importance of each influencing factors.

RESULTS

The results showed that the prevalence of anxiety among the elderly was 11.24%. Anxiety was mainly associated with 14 factors from five aspects: sociodemographic characteristics, health status, psychological state, social trust and social participation, among which loneliness related to psychological status was the most important factor.

DISCUSSION

The revelation of this study is that the present situation of anxiety among the elderly cannot be ignored, and it is necessary to take measures to prevent and control it from many aspects.

The Impact of Multidimensional Poverty on Rural Households' Health: From a Perspective of Social Capital and Family Care

Although absolute poverty has been eliminated in rural China, multidimensional poverty has an unstoppable impact on the self-rated health of rural households through multiple dimensions. This study constructed a moderated mediation model with multidimensional poverty as the independent variable to explore the impact on rural households' self-rated health, social capital as a mediating variable, and family care as a moderating variable. We used the survey data of 382 sample out-of-poverty rural households in Jiangxi, China, in 2020. Our results indicated that multidimensional poverty had a detrimental impact on the self-rated health and social capital of rural households, both of which were significant at the 1% level (β = -0.751, t = -4.775, and β = -0.197, t = -7.08). A test of the mediating effect of social capital using the mediation model found the mediating effect accounting for 84.95% of the entire effect of multidimensional poverty on rural households' self-rated health. Further, the interaction term between family care and multidimensional poverty and its beneficial effect on social capital as well as the interaction term between family care and social capital and its negative effect on rural household' self-rated health are both statistically significant at the 1% level (β = 0.558, t = -5.221 and β = -2.100, t = -3.304). It is revealed that multidimensional poverty affects rural households' self-rated health through social capital and that family care moderates the mediating pathway. Family care exacerbates the negative effect of multidimensional poverty on rural households' self-rated health and weakens the beneficial effect of social capital on rural households' self-rated health. The lower (higher) the level of family care, the more significant the positive (negative) effect of social capital on rural households' health. Therefore, rural households should prioritize building social capital and shifting the responsibility for family care. First, through enhancing housing infrastructure and establishing cultural and educational initiatives, households can improve their viability. Second, increasing engagement in group activities will enhance social networks and boost interpersonal connections. Finally, to lessen the stress on family caregivers, building socialized care services can cover the gap in family care.

Spatiotemporal Pattern of Urban-Rural Integration Development and Its Driving Mechanism Analysis in Hangzhou Bay Urban Agglomeration

The quantitative analysis of the urban-rural integration development (URID) level and its driving factors is of great significance for the new-type urbanization of urban agglomerations. This study constructed a multidimensional framework in the perspective of a population-space-economy-society-ecology framework to measure the URID level from 2000 to 2020 and further explored the driving mechanism of the URID changes by a geographical detector model in the Hangzhou Bay urban agglomeration (HBUA). The results showed that the land-use change in the HBUA from 2000 to 2020 showed a typical characteristic of the transition between cultivated and construction land. The URID level in the HBUA improved from 0.294 in 2000 to 0.563 in 2020, and the year 2005 may have been the inflection point of URID in the HBUA. The URID level showed a significant spatial aggregation with high values. Hangzhou, Jiaxing, and Ningbo were hot spots since 2015, and the cold spots were Huzhou and Shaoxing. The population and spatial integration had more important impacts on URID levels in 2000, 2005, and 2020, while economic and social integration had more significant impacts on URID levels in 2010 and 2015. This study provided a deeper understanding of the evolution of URID in an urban agglomeration and could be used as a reference for decision makers.