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Pre-pandemic research demonstrated the challenges of the nursing workforce and the provision of quality of patient care. Such challenges have been significantly intensified during the COVID-19 pandemic, not least in the workplace and fear of staff catching and transmitting COVID-19. We draw on conservation of resources (COR) theory to examine the impact of the fear of COVID-19 on nurses and the role of well-being-HRM (WBHRM) in negating the fear of COVID-19 and its impact on job stress and perceived quality of patient care.

We collected data from 260 nurses (treating COVID-19 patients) employed in US hospitals across two-waves. Data were analyzed using mediated regression and moderated mediation.

The results indicated that when nurses report higher levels of fear of COVID-19, this translates into higher levels of nursing job stress. This, in turn, reduces nurses’ perceptions of quality of patient care they can provide. As previous research has found, decreased perceptions of quality of patient care is a significant factor driving intentions to leave the profession. The results demonstrated that WBHRM practices buffer the negative impact of fear of COVID-19 on job stress, and in turn, the perceived quality of patient care.

Our paper contributes to new knowledge for healthcare managers on WBHRM bundles and their efficacy in buffering the effects of fear on job stress and quality of patient care. We contribute new knowledge on fear at work and how to manage employees’ fear through WBHRM practices.

This study aims to adopt text mining to discover emerging topics in librarianship and information science research in the last decade. Based on the number of citations obtained during the previous 10 years, the authors selected emerging topics in this study and evaluated the strength of their presence. Additionally, the authors determined if the trend was substantial over time and identified the active topics in library and information science (LIS) through the past 10 years.

All library and medical information studies were retrieved by the WC = “Information Science & Library Science” tag in the Web of Science. Python programming was used for data analysis. The topics were identified by combining the unsupervised deep learning algorithms TOP2VEC and the term frequency-inverse document frequency and also the Mann–Kendall trend test is used to determine whether the trend was significant over time.

Following text mining, the total data from 2012 to 2021 was 63,712. Eleven main topics were also extracted: academic education of LIS, acquisition and collection development, publishing articles, cataloging and classification, journalism, knowledge management, infometrics, social media, university ranking, information and communication technologies and information storage and retrieval. Knowledge management has experienced the greatest growth over the past 10 years.

This analysis reveals which fields are prioritized and which are neglected by the LIS. The findings of this study can help researchers discover newer topics, focus on less-seen subjects and prevent repetitive research in one area.