Introduction: The integration of artificial intelligence (AI) in intensive care units (ICUs) presents both opportunities and challenges for critical care nurses. This study delves into the human factor, exploring how nurses with leadership roles perceive the impact of AI on their professional practice. Objective: To investigate how nurses perceive the impact of AI on their professional identity, ethical considerations surrounding its use, and the shared meanings they attribute to trust, collaboration, and communication when working with AI systems. Methods: An interpretive phenomenological analysis was used to capture the lived experiences of critical care nurses leading with AI. Ten nurses with leadership roles in various ICU specializations were interviewed through purposive sampling. Semi-structured interviews explored nurses’ experiences with AI, challenges, and opportunities. Thematic analysis identified recurring themes related to the human factor in leading with AI. Findings: Thematic analysis revealed two key themes which are leading with AI: making sense of challenges and opportunities and the human factor in leading with AI. The two main themes have six subthemes which revealed that AI offered benefits like task automation, but concerns existed about overreliance and the need for ongoing training. New challenges emerged, including adapting to new workflows and managing potential bias. Clear communication and collaboration were crucial for successful AI integration. Building trust in AI hinged on transparency, and collaboration allowed nurses to focus on human-centered care while AI supported data analysis. Ethical considerations included maintaining patient autonomy and ensuring accountability in AI-driven decisions. Conclusion: While AI presents opportunities for automation and data analysis, successful integration hinges on addressing concerns about overreliance, workflow adaptation, and potential bias. Building trust and fostering collaboration are fundamentals for AI integration. Transparency in AI systems allows nurses to confidently delegate tasks, while collaboration empowers them to focus on human-centered care with AI support. Ultimately, dealing with the ethical concerns of AI in ICU care requires prioritizing patient autonomy and ensuring accountability in AI-driven decisions.

Background: The coronavirus disease 2019 (COVID‐19) pandemic directly affects the psychological well‐being of critical care nurses. Several studies had investigated the psychological impact of the pandemic on nurses caring for patients with COVID‐19, but few were conducted to identify the predictors of this impact.Aims: The objective of this study is to identify the predictors of critical care nurses' stress, anxiety, and depression in response to the COVID‐19 pandemic.Design: A cross‐sectional survey was conducted in five intensive care units in five hospitals in Alexandria, Egypt.Methods: An online questionnaire was distributed. It included socio‐demographic and work‐related data and the depression, anxiety, and stress scale scores of the nurses under study. A multiple linear regression model was developed to identify the predictors of critical care nurses' stress, anxiety, and depression in response to the COVID‐19 pandemic.Results: Two hundred (64%) of 308 nurses completed the electronic questionnaire. Significant predictors of stress included the number of infected colleagues (P < .001) and availability of hospital resources (P = .01). Significant predictors of anxiety were age, gender, satisfactory income (P < .001), years of experience, time spent caring for patients with COVID‐19 (P = .04), continuous training, number of infected colleagues (P = .01), and availability of hospital resources (P = .02). Finally, significant predictors of depression included gender, history of physical problems (P = .04), educational attainment, availability of hospital resources, history of psychological problems (P < .001), and number of infected colleagues (P = .001).Conclusion: The hospital's lack of human and physical resources and the number of colleagues infected with COVID‐19 were the strongest predictors of stress, anxiety, and depression among nurses.Relevance to clinical practice: Identifying the predictors of stress, anxiety, and depression among nurses who care for patients with COVID‐19 is a vital step in developing mental health promotion strategies to support nurses during this pandemic.

Background: Resilience is essential for nurses in critical care, where they face high-stakes situations requiring continuous adaptation, collaborative unity, and emotional balance. This study aimed to investigate resilience in critical care nursing, focusing on how nurses adapt to challenges, the role of teamwork in fostering resilience, and strategies for maintaining emotional balance. Objective: The objective of this study was to explore resilience among critical care nurses, emphasizing their adaptation to challenges, the influence of collaborative practices, and methods for sustaining emotional well-being in intensive care units. Methods: A qualitative study with a thematic analysis approach was used in this study. The study was conducted in five intensive care units across two hospitals in Egypt. The purposeful sampling approach includes 17 critical care nurses with diverse experiences. Data was collected through in-depth semi-structured interviews using an interview guide focusing on challenges, adaptation strategies, collaborative practices, and coping mechanisms. Results: Themes emerge, depicting resilience as a dynamic process encompassing continuous adaptation, learning, collaborative unity, emotional balance, self-care, and reflection on experiences. Nurses emphasize the importance of teamwork, interprofessional collaboration, and managing emotional complexities. Resilience is portrayed as a collective force within the critical care team, balancing compassion and clinical precision. Conclusion: Critical care nurses demonstrate resilience as a multifaceted and dynamic process. The study provides insights into the collaborative strategies employed and the emotional aspects of resilience. Acknowledging vulnerabilities and prioritizing self-care are integral to sustaining resilience.

Background: Blended learning, a pedagogical approach combining traditional classroom instruction with online components, has gained prominence in nursing education. While offering numerous benefits, student satisfaction with blended learning remains a critical concern. This study contributes to the existing literature by providing a comprehensive evaluation of the determinants influencing nursing students’ satisfaction with this innovative educational modality. By examining a wide range of factors, including sociodemographic characteristics, academic factors, and environmental influences, this research offers valuable insights for educators to optimize blended learning experiences in nursing education. Methods: A descriptive cross-sectional research design was conducted. This study investigates the factors influencing nursing students’ satisfaction with blended learning at Alexandria University, Egypt, where blended learning programs have been integrated into the curriculum primarily through the Microsoft Teams platform. A convenient sample of 1266 nursing students from both bachelor and technical educational institutions participated in the study from September 2023 to the end of December 2023. Data were collected using an online survey containing two measurement tools: the Blended Learning Satisfaction Scale and the Environmental Facilitators and Barriers to Student Persistence in Online Courses scale. Statistical analyses, including descriptive statistics and backward multiple linear regression, were conducted to identify factors that are associated with the satisfaction of nursing students’ with blended learning. Results: Findings indicate that factors such as age, gender, income, employment status, access to suitable internet sources, academic year, computer literacy, preferred learning method, and perceptions of environmental facilitators significantly influence satisfaction scores (all p < 0.001). The overall regression model, with an adjusted R² of 0.31, signifies that 31% of the variance in satisfaction scores is explained collectively by the previously mentioned variables (F = 21.21, p < 0.001). Conclusion: Students’ sociodemographic variables, preference for blended learning, and perception of environmental facilitators such as encouragement to enroll in the course significantly influence nursing students’ satisfaction levels with blended learning. However, limitations in the current study such as self-report bias, convenient sampling, and cross-sectional design limit the generalizability and causal inferences of these findings.