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In the wake of the COVID-19 pandemic, the fluctuating nurse resignation rates highlighted an understudied area in healthcare: post-pandemic challenges in clinical settings. This study, conducted from May to November 2023, employed a qualitative inquiry using focus groups to delve into these challenges. Six focus group sessions, involving 33 nurse participants recruited through snowball sampling from various hospital settings were conducted to explore their clinical experiences during and after the pandemic. Thematic analysis revealed two primary themes: the 'Invisibility of Nurses' within the healthcare system and the 'Moral Duty of Nursing Practice'. These findings illuminate a tension between the overlooked role of nurses and their ethical obligations, underscoring a critical need for policy reassessment. The study advocates for systemic changes, particularly in the undervaluation of the nursing profession and the National Health Insurance system, to address the poor working environment and mitigate long-term nursing shortages. This research deepens understanding of post-pandemic nursing workforce challenges in Taiwan, highlighting the need for policy evolution to enhance recognition and support for the nursing industry. It is suggested to provide tangible compensation to acknowledge nurses' daily care and health education for patients. A healthier working environment can be enhanced by collaborative efforts between healthcare institutions and nurses.

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research. [ABSTRACT FROM AUTHOR]

As populations age, average life expectancy increases and the complexity of diseases rises, leading to nursing care and healthcare systems facing severe challenges related to inadequate resources. Artificial intelligence (AI), including elements such as investigation, integration, learning, prediction, and decision-making, holds significant potential for application in clinical care not only to enhance care quality but also to help guide the future direction of healthcare. AI applications are already being increasingly utilized to improve the quality of clinical care and to streamline workflows. However, because nursing education has lagged behind in terms of adopting AI, greater attention must be given to training up nursing students with AI-related knowledge and application skills. AI technologies should be integrated into nursing curricula and clinical internships to adapt to the rapidly changing high-tech healthcare environment, enabling the more-effective use of AI technology in providing high-quality and safe nursing care. [ABSTRACT FROM AUTHOR]

This article underscores the importance of integrating comprehensive palliative care for noncancer patients who are undergoing hemodialysis, with an emphasis on the aging populations in Asian nations such as Taiwan, Japan, the Republic of Korea, and China. As the global demographic landscape shifts towards an aging society and healthcare continues to advance, a marked increase has been observed in patients undergoing hemodialysis who require palliative care. This necessitates an immediate paradigm shift to incorporate this care, addressing the intricate physical, psychosocial, and spiritual challenges faced by these individuals and their families. Numerous challenges impede the provision of effective palliative care, including difficulties in prognosis, delayed referrals, cultural misconceptions, lack of clinician confidence, and insufficient collaboration among healthcare professionals. The article proposes potential solutions, such as targeted training for clinicians, the use of telemedicine to facilitate shared decision-making, and the introduction of time-limited trials for dialysis to overcome these obstacles. The integration of palliative care into routine renal treatment and the promotion of transparent communication among healthcare professionals represent key strategies to enhance the quality of life and end-of-life care for people on hemodialysis. By embracing innovative strategies and fostering collaboration, healthcare providers can deliver more patient-centered, holistic care that meets the complex needs of seriously ill patients within an aging population undergoing hemodialysis. [ABSTRACT FROM AUTHOR]

Background: Deaths in the community are increasing. However, community palliative care out-of-hours is variable. We lack detailed understanding of how care is provided out-of-hours and the associated outcomes. Aim: To review systematically the components, outcomes and economic evaluation of community-based ‘out-of-hours’ care for patients near the end of life and their families. Design: Mixed method systematic narrative review. Narrative synthesis, development and application of a typology to categorise out-of-hours provision. Qualitative data were synthesised thematically and integrated at the level of interpretation and reporting. Data sources: Systematic review searching; MEDLINE, EMBASE, PsycINFO, CINAHL from January 1990 to 1st August 2022. Results: About 64 publications from 54 studies were synthesised (from 9259 retrieved). Two main themes were identified: (1) importance of being known to a service and (2) high-quality coordination of care. A typology of out-of-hours service provision was constructed using three overarching dimensions (service times, focus of team delivering the care and type of care delivered) resulting in 15 categories of care. Only nine papers were randomised control trials or controlled cohorts reporting outcomes. Evidence on effectiveness was apparent for providing 24/7 specialist palliative care with both hands-on clinical care and advisory care. Only nine publications reported economic evaluation. Conclusions: The typological framework allows models of out-of-hours care to be systematically defined and compared. We highlight the models of out-of-hours care which are linked with improvement of patient outcomes. There is a need for effectiveness and cost effectiveness studies which define and categorise out-of-hours care to allow thorough evaluation of services. [ABSTRACT FROM AUTHOR]