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Aims: During the COVID-19 epidemic, nurses played a crucial role in clinical treatment. As a special group, front-line nurses, especially those assigned to support Hubei Province in the fight against COVID-19 between February and April 2020, brought diverse experiences from different provinces in China in taking care of COVID-19 patients and role cognition. Therefore, our purpose is to explore the real coping experience and role cognition of front-line nurses during the novel coronavirus outbreak to provide relevant experience references for society and managers in the face of such major public health emergencies in the future. Design: This qualitative study was performed using the phenomenological hermeneutics method. Method: This is a qualitative phenomenological study. Semi-structured in-depth interviews were used to collect data. The interviewees were 53 front-line nurses who assisted and supported the fight against COVID-19 in Hubei Province during the COVID-19 epidemic. Data were collected through individual online and telephone interviews using a semi-structured interview during March 2020. The COREQ guidance was used to report this study. Results: The findings revealed that front-line nurses assisting in the fight against COVID-19 developed a context-specific role cognition of their work and contribution to society. The qualitative analysis of the data revealed 15 subcategories and 5 main categories. These five themes represented the different roles identified by nurses. The roles included expectations, conflicts, adaptation, emotions, and flow of blessing. Belief in getting better, a sense of honor, and training could help them to reduce feelings of conflict in this role and adapt more quickly. Discussion: This article discusses the real coping experience and role cognition of front-line nurses during the novel coronavirus epidemic. It provides relevant experience references for society and managers to face similar major public health emergencies in the future. This study makes a significant contribution to the literature because it demonstrates how non-local nurses sent to Hubei to work perceived their roles as part of a larger narrative of patriotism, duty, solidarity, and hope. [ABSTRACT FROM AUTHOR]

We introduce a class of Bayesian infinite mixture models first introduced by Lo (1984) to determine the credibility premium for a non-homogeneous insurance portfolio. The Bayesian infinite mixture models provide us with much flexibility in the specification of the claim distribution. We employ the sampling scheme based on a weighted Chinese restaurant process introduced in Lo et al. (1996) to estimate a Bayesian infinite mixture model from the claim data. The Bayesian sampling scheme also provides a systematic way to cluster the claim data. This can provide some insights into the risk characteristics of the policyholders. The estimated credibility premium from the Bayesian infinite mixture model can be written as a linear combination of the prior estimate and the sample mean of the claim data. Estimation results for the Bayesian mixture credibility premiums will be presented. [ABSTRACT FROM AUTHOR]