Learning to combat social isolation in senior living communities during COVID-19 and beyond

Social isolation has been a growing concern since the onset of the COVID-19 pandemic, disproportionately impacting older adults. Social isolation can impact the physical, mental, and emotional health of older adults. The purpose of this study was to examine coping strategies of older adults living in senior living communities, as well as the supportive efforts of the team members working in such communities, to determine best practices for combating social isolation for older adults. This qualitative study was guided by the research question: How do older adults perceive loneliness, social isolation, and social connectedness living in senior living communities? Guided by the theoretical frameworks of socioemotional selectivity theory (SST) and strength and vulnerability integration model (SAVI), this study explored how diminished time horizons impact the prioritization of social connections. This qualitative study collected data through semi-structured interviews from older adults living in senior living communities in the United States. Several themes emerged from the data including Parameters of Social Connection, Dining Room as a Hub of Socialization, Time Horizon Awareness and Compensation, Strategies of Connection, and Loss of Spouse. Several implications for best practices are also discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Age differences in psychological distress during the COVID-19 pandemic: March 2020 - June 2021.

In March 2020, COVID-19 brought illness, lockdowns, and economic turmoil worldwide. Studies from March-April 2020 reported increased psychological distress, especially among younger (vs. older) adults. Here, we examine whether age differences persisted in a 29-wave longitudinal survey conducted with an American national life-span sample over the first 16 months of the pandemic. Socio-emotional selectivity theory (SST) predicts that older age will be consistently associated with lower psychological distress due to life-span changes in motivation, while the strength and vulnerability integration model (SAVI) posits that age differences in psychological distress will diminish under prolonged stress. We find that younger adults consistently reported more psychological distress than older adults, though age differences did decrease over time. Prior diagnosis with anxiety or depression additionally predicted greater psychological distress throughout the study, but did not moderate age differences. We discuss implications for psychological theories of aging and interventions to reduce psychological distress.

Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.

Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient's metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely-direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The proposed framework helps clinicians diagnose lung inflammation and provide an end-to-end robust diagnostic system. Initially, the feasibility of integrating patients' symptoms, clinical pathologies, and radiologist feedback with CAD system to improve the classification performance is investigated. Subsequently, the patient's metadata and radiologist feedback are integrated with the CAD system using the proposed integration frameworks. The proposed method's performance is evaluated using a private dataset consisting of 70 chest X-ray (CXR) images (31 COVID-19, 14 other diseases, and 25 normal). The obtained results reveal that the proposed WBI achieved the highest classification performance (accuracy = 98.18%, F1 score = 97.73%, and Matthew's correlation coefficient = 0.969) compared to DI and RI. The generalization capability of the proposed framework is also verified from an external validation set. Furthermore, the Friedman average ranking and Shaffer and Holm post hoc statistical methods reveal the obtained results' statistical significance. Methodological diagram of proposed integration frameworks.