Original Research: Exploring U.S. Nurses' Perceived Duty to Care During the COVID-19 Pandemic.

BACKGROUND: Duty to care is both an integral concept in health care and a fundamental nursing obligation. But nurses' perceptions of duty to care can be moderated by their experiences in the practice setting. Research examining nurses' perceptions of their duty to care during the COVID-19 pandemic could shed light on how the pandemic is affecting the nursing workforce. PURPOSE: This study aimed to examine nurses' sense of duty to care during the early months of the pandemic, using the Nash Duty to Care Scale (NDCS), and to compare the high-scoring nurses with the low-scoring nurses. METHODS: This quantitative study used a descriptive, cross-sectional design. It was conducted among licensed RNs enrolled at two accredited nursing programs in the Northeast region of the United States. Data were collected via a demographics questionnaire and the NDCS. A two-step cluster procedure was used to categorize participants into two groups: those with high perceived duty to care (HPDC) and those with low perceived duty to care (LPDC). Independent t tests were performed to compare NDCS results between the two groups. RESULTS: Nearly two-thirds (61%) of the participants had total NDCS scores indicating an HPDC, while 39% had scores indicating an LPDC. Of the NDCS's four subscales, perceived obligation and perceived risk were the most important in separating participants into the low- and high-scoring groups. CONCLUSIONS: This study adds to the literature about the components that affected nurses' perceived duty to care and willingness to report to work during the early months of the pandemic. Just as nurses have a duty to care, health care organizations have an obligation to provide a safe working environment so that nurses can fulfill that duty without sacrificing personal safety. The study findings may guide health care leaders, systems, and organizations regarding how to create safer work environments that support the nurse's duty to care during disasters.

A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine.

Digital health and telemonitoring have resulted in a wealth of information to be collected to monitor, manage, and improve human health. The multi-source mixed-frequency health data overwhelm the modeling capacity of existing statistical and machine learning models, due to many challenging properties. Although predictive analytics for big health data plays an important role in telemonitoring, there is a lack of rigorous prediction model that can automatically predicts patients' health conditions, e.g., Disease Severity Indicators (DSIs), from multi-source mixed-frequency data. Sleep disorder is a prevalent cardiac syndrome that is characterized by abnormal respiratory patterns during sleep. Although wearable devices are available to administrate sleep studies at home, the manual scoring process to generate the DSI remains a bottleneck in automated monitoring and diagnosis of sleep disorder. To address the multi-fold challenges for precise prediction of the DSI from high-dimensional multi-source mixed-frequency data in sleep disorder, we propose a sparse linear mixed model that combines the modified Cholesky decomposition with group lasso penalties to enable joint group selection of fixed effects and random effects. A novel Expectation Maximization (EM) algorithm integrated with an efficient Majorization Maximization (MM) algorithm is developed for model estimation of the proposed sparse linear mixed model with group variable selection. The proposed method was applied to the SHHS data for telemonitoring and diagnosis of sleep disorder and found that a few significant feature groups that are consistent with prior medical studies on sleep disorder. The proposed method also outperformed a few benchmark methods with the highest prediction accuracy.