ABSTRACT
Nurses play a pivotal role in the delivery of medical services. Professional commitment is crucial for nursing professionals' long-term, healthy, and sustainable development. However, nursing students' professional commitment levels are currently unsatisfactory in China, especially given that the COVID-19 pandemic has posed unprecedented challenges to the profession. Therefore, studies investigating the professional commitment levels of nursing students and the underlying influencing factors are urgently required. This study explored how nursing students' risk perceptions, negative emotions, and psychological capital affected their professional commitment during the COVID-19 pandemic. A cross-sectional study was conducted among nursing students using risk perception, professional commitment, negative emotions, and psychological capital scales. An analysis of 1142 Chinese nursing students suggested that nursing students' risk perception positively impacted professional commitment and that negative emotions mediated this association. Importantly, psychological capital moderates the mediating effect of negative emotions and can buffer the negative emotions caused by risk perception. This study demonstrated that effective intervention strategies should be implemented in multiple dimensions such as education, individual, public and society to improve the professional commitment of nursing students.
ABSTRACT
PURPOSE: Hospitals suffered from a precipitous loss of medical service globally due to COVID-19. The tragedy paradoxically produced an opportunity to investigate the patterns of change in medical services and revenue in hospitals at different levels when faced with a natural shock. This study aims to examine the effects of the COVID-19 pandemic in the first half of 2020 on hospital operation in Shanghai. METHODS: We obtained monthly characteristic and operational data of public hospitals (N=156) from January 1, 2018, to July 31, 2020, in Shanghai from the China Statistical Survey of Health Resources and Services Program. We constructed a set of difference-in-differences models to investigate the pandemic (from February 1 to March 31, 2020) and post-pandemic (from April 1 to July 31, 2020) effects on operational outcomes in hospitals of different levels, including outpatient and inpatient visits, outpatient and inpatient revenue, as well as the differential effects on local and nonlocal patients. RESULTS: There were 46 tertiary hospitals and 110 non-tertiary hospitals involved in this study. Compared to a non-tertiary hospital during the COVID-19 pandemic, a tertiary hospital averagely experienced substantially more significant losses in outpatient visits (57.91 thousand, p < 0.01), inpatient visits (1.93 thousand, p < 0.01), outpatient revenue (18.88 million RMB, p < 0.01), and inpatient revenue (30.65 million RMB, p < 0.01) monthly. Compared to a non-tertiary hospital in the post-pandemic period, a tertiary hospital averagely lost more outpatient visits (18.02 thousand, p < 0.01) from all patients and inpatient visits (0.15 thousand, p < 0.01) from nonlocal patients, but was associated with higher inpatient revenue (2.24 million RMB, p < 0.01) from all patients and outpatient revenue (0.87 million RMB, p < 0.01) from nonlocal patients monthly. CONCLUSION: Medical service and revenue for public hospitals in Shanghai dropped precipitously during the COVID-19 pandemic, but mainly recovered after the pandemic. Compared to non-tertiary hospitals, medical services and revenue in tertiary hospitals experienced more substantial reduction during the pandemic but had a faster recovery that maintained longer during the post-pandemic period.
ABSTRACT
BACKGROUND: This study aims to compare the epidemiological, clinical and laboratory characteristics between patients with coronavirus disease (COVID-19) and influenza A (H1N1), and to develop a differentiating model and a simple scoring system. METHODS: We retrospectively analyzed the data from patients with COVID-19 and H1N1. The logistic regression model based on clinical and laboratory characteristics was constructed to distinguish COVID-19 from H1N1. Scores were assigned to each of independent discrimination factors based on their odds ratios. The performance of the prediction model and scoring system was assessed. RESULTS: A total of 236 patients were recruited, including 20 COVID-19 patients and 216 H1N1 patients. Logistic regression revealed that age >34 years, temperature ≤37.5 °C, no sputum or myalgia, lymphocyte ratio ≥20% and creatine kinase-myocardial band isoenzyme (CK-MB) >9.7 U/L were independent differentiating factors for COVID-19. The area under curves (AUCs) of the prediction model and scoring system in differentiating COVID-19 from H1N1 were 0.988 and 0.962, respectively. CONCLUSIONS: There are certain differences in clinical and laboratory features between patients with COVID-19 and H1N1. The simple scoring system may be a useful tool for the early identification of COVID-19 patients from H1N1 patients.