Aim and objectives: To assess the prevalence of anxiety, sleep disorders and self‐efficacy and their predicting factors among nurses facing COVID‐19.Background: The spread of COVID‐19 throughout the world determined a series of modifications of several National Health Service organisations, with a potential series of psychological consequences among nurses, who were particularly afflicted by this situation of changes and precariousness.Design: A cross‐sectional study was carried out from February–April 2020.Methods: A total of 1,005 nurses employed in different Italian hospital wards, during the COVID‐19 pandemic, were recruited. Analyses were based on descriptive statistics and multivariate logistic regression. The STROBE checklist for cross‐sectional studies was used in this study.Results: The prevalence of sleep disturbances, moderate anxiety and low self‐efficacy was 71.4%, 33.23% and 50.65%, respectively. We found a positive correlation between anxiety and sleep quality (0.408; p < .0001) and negative correlations between self‐efficacy and anxiety (−0.217; p < .0001) and sleep quality and self‐efficacy (−0.134; p < .0001).The factor independently associated with all variables was gender. Females were more prone to sleep disturbances, anxiety and low levels of self‐efficacy than males (p < .05).Conclusions: The prevalence of anxiety, sleep disorders and low self‐efficacy among Italian nurses during the COVID‐19 pandemic was high. Healthcare managers should recognise and consider these results to reduce the risk of the onset of major mental problems that could result in post‐traumatic stress disorder.Relevance to clinical practice: Nurses facing major incidents as COVID‐19 pandemic are among healthcare personnel exposed to a high risk to develop psychological disturbance that should be assessed and recognised, in order to find helpful coping strategies to inform support services and avoid to hesitate in post‐traumatic stress disorders.

Aim The aim of this study was to explore the experience of Italian nurses engaged in caring for patients with COVID‐19. Background COVID‐19 found the healthcare world unprepared to face an emergency of such magnitude. Italy was one of the most affected European countries, with more than 250,000 cases. Understanding the impact of events of this magnitude on nurses provides a framework of knowledge on which educational training could be based to face similar situations in the future to prevent further breakdown. Methods The hermeneutic approach by Cohen was used. Semi‐structured interviews were conducted using a voice‐over Internet protocol. Interviews were transcribed, read in depth, and analysed. Results Twenty nurses were interviewed. Four themes were extracted: uncertainty and fear, alteration of perceptions of time and space, change in the meaning of ‘to care’, and changes in roles and relationships. Conclusions Psychological support in association with emergency training prevent stress and help tackle compassion fatigue. Implications for nursing management Policies to improve nursing science should be developed to ensure better quality of care, a higher number of professionals, and, consequently, an increase in the safety of patients.

Aim: To review the qualitative literature regarding how people with fibromyalgia experience and are impacted by stigma.Design: A systematic review and metasynthesis of qualitative studies was conducted following the Thomas and Harden method.Methods: The electronic databases PubMed, CINAHL, PsycInfo, Embase and Scopus were queried (September 2023). No publication year limit was set. Twelve studies were included in the final analysis. The findings were reported according to the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement.Findings: Three main themes with 11 sub‐themes were identified: manifestations and roots of stigma, avoidance and coping strategies, and consequences of stigma. Stigma arises from disbelief due to the invisibility of symptoms, protracted time to diagnosis and gender stereotyping, especially against women. Various strategies to avoid or cope with prejudice may involve social isolation, hiding the disease, controlling information, getting closer to or further away from other patients, acknowledging and understanding the disease. Stigmatisation can diminish a person's integrity and dignity, undermine trust in health care professionals and worsen suffering.Conclusion: The metasynthesis findings align with previous research highlighting the pervasive stigma associated with chronic pain conditions. Greater awareness of the impact of disease‐related stigma on individuals with fibromyalgia is crucial, not only among health care professionals but also within broader societal and institutional contexts.Implications for the Profession and/or Patient Care: Understanding the stigma experienced by individuals with fibromyalgia can guide health care professionals in adopting more empathtic approaches, potentially improving the diagnostic process and the overall management of the condition.Impact: This study highlights the profound impact of stigma on individuals with fibromyalgia, emphasising the need for greater awareness and targeted interventions to address stigma in clinical practice and societal contexts.Patient or Public Contribution: No patient or public contribution.

In recent years, the increase in textual data production has meant that researchers require faster text analysis techniques and software to reliably produce knowledge for the scientific–nursing community. Automatic text data analysis opens the frontiers to a new research area combining the depth of analysis typical of qualitative research and the stability of measurements required for quantitative studies. Thanks to the statistical–computational approach, it proposes to study more or less extensive written texts produced in natural language to reveal lexical and linguistic worlds and extract useful and meaningful information for researchers. This article aims to provide an overview of this methodology, which has been rarely used in the nursing community to date.

The integration of artificial intelligence (AI) into healthcare marks a paradigmatic shift, raising not only technical and organizational challenges but also profound ethical questions. This theoretical-reflective paper explores the ethical dimensions of AI from a nursing perspective, focusing on the role of nurse leaders as ethical agents in the digital transformation of care. Drawing on philosophical and ethical frameworks, such as Heidegger’s notion of humans as “sensors of machines” and the concept of algorethics , the article argues that AI should not replace the relational, interpretive, and moral dimensions of care, but rather support them, provided its implementation is guided by ethical vigilance. The World Health Organization’s principles—autonomy, beneficence, transparency, accountability, justice, and sustainability—serve as the ethical foundation for AI in healthcare, highlighting the need to preserve human dignity and prevent discrimination. Yet algorithmic opacity, automation bias, and data-driven inequities demand renewed attention to responsibility, explainability, and the preservation of human agency. Nurse leaders are called to act along three ethical pillars: fostering transparency in algorithmic processes, ensuring shared responsibility for AI-supported decisions, and sustaining human relationships as the core of care. By reclaiming relational time (kairos) and resisting the delegation of moral judgment to machines, nurses safeguard the human face of care. In doing so, they embody a leadership rooted in presence, justice, and ethical discernment. Ultimately, this paper calls for a proactive governance of AI that anticipates its impacts and ensures its alignment with the telos of nursing: to care for persons with competence, conscience, and compassion.