Neonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its impact on nurses’ decision-making, professional autonomy, and organizational workflows. This study explored how neonatal nurses experience and integrate generative AI in clinical decision-making, examining its influence on nursing practice, organizational dynamics, and cultural adaptation in Saudi Arabian NICUs. An interpretive phenomenological approach, guided by Complexity Science, Normalization Process Theory, and Tanner’s Clinical Judgment Model, was employed. A purposive sample of 33 neonatal nurses participated in semi-structured interviews and focus groups. Thematic analysis was used to code and interpret data, supported by an inter-rater reliability of 0.88. Simple frequency counts were included to illustrate the prevalence of themes but were not used as quantitative measures. Trustworthiness was ensured through reflexive journaling, peer debriefing, and member checking. Five themes emerged: (1) Clinical Decision-Making, where 93.9% of nurses reported that AI-enhanced judgment but required human validation; (2) Professional Practice Transformation, with 84.8% noting evolving role boundaries and workflow changes; (3) Organizational Factors, as 97.0% emphasized the necessity of infrastructure, training, and policy integration; (4) Cultural Influences, with 87.9% highlighting AI’s alignment with family-centered care; and (5) Implementation Challenges, where 90.9% identified technical barriers and adaptation strategies. Generative AI can support neonatal nurses in clinical decision-making, but its effectiveness depends on structured training, reliable infrastructure, and culturally sensitive implementation. These findings provide evidence-based insights for policymakers and healthcare leaders to ensure AI integration enhances nursing expertise while maintaining safe, patient-centered care.

Workplace violence (WPV) remains a formidable concern among nurses worldwide, with up to 60% in Saudi Arabia reportedly experiencing some form of aggression. In tertiary care hospitals, robust hierarchies and cultural norms intensify underreporting, thwarting evidence-based prevention and obscuring vital data. This qualitative study investigated the perceived barriers to WPV reporting among nurses in tertiary care settings in the Aljouf region of Saudi Arabia, specifically addressing how organizational and cultural factors converge to discourage formal incident reporting. A qualitative descriptive design was employed, guided by Ajzen’s Theory of Planned Behavior and the Social Ecological Model. Thirty-six registered nurses, purposively sampled from three tertiary hospitals, participated in six semi-structured focus groups conducted in Arabic or English, depending on participant preference. Data were thematically analyzed in NVivo, with methodological rigor ensured through triangulation and inter-coder reliability. Three principal themes emerged: (1) Emotional and Psychological Barriers (78%), encompassing distress, anxiety, and fears of professional blame; (2) Organizational Ineffectiveness (65%), marked by convoluted reporting processes and perceived managerial indifference; and (3) Cultural and Hierarchical Influences (57%), reflecting deference to authority and normalization of violence. These themes illustrate how attitudes, subjective norms, and perceived behavioral control shaped by socio-cultural dynamics collectively contribute to persistent underreporting. Mitigating WPV underreporting in Saudi tertiary care hospitals requires streamlined, user-friendly reporting channels, leadership accountability, and holistic psychosocial support. Implementing interprofessional education aimed at dismantling hierarchical imbalances can foster a zero-tolerance ethos toward violence. Longitudinal and comparative research should further examine evolving reporting behaviors to refine context-specific, culturally attuned strategies for addressing WPV. Not applicable.

Background: The rapid advancements in artificial intelligence (AI) technologies across various sectors, including health care, necessitate the need for a comprehensive understanding of their applications. Specifically, the acceptance and readiness of nursing students as future health care professionals to adopt AI-based health care technologies, along with the factors influencing these attitudes, are critical for facilitating the effective integration of AI in health care settings. Objective: This study aimed to assess the readiness and acceptance of nursing students regarding the use of AI-based health care technologies in the nursing skills training in Saudi Arabia. Methods: A descriptive cross-sectional research design was used. A convenience sampling technique was applied to recruit 322 participants. Data were collected between June and September 2023 using a self-administered questionnaire that included the technology readiness index (TRI) and the technology acceptance scale. Results: Approximately 92.2% (297/322) of participants exhibited positive attitudes toward AI, and 74.8% (322) demonstrated innovativeness, indicating a generally favorable perception of AI. However, more than half of the students (59% [190/322] and 59.3% [191/322], respectively) reported feelings of discomfort and negative perceptions regarding AI use. Regarding TRI, 69.6% (224/322) of participants showed moderate readiness, while 30.4% (98/322) exhibited a high level of TRI. A substantial majority (320/322 99.4%) expressed acceptance of AI-based technologies in their training, with only 0.6% (2/322) reporting nonacceptance. Older students (aged >22 y) exhibited significantly higher levels of AI acceptance and readiness compared to younger students (P<.001). In addition, female students demonstrated significantly greater readiness and acceptance levels than male students (P=.003). Further, third-level students reported the highest mean scores in both acceptance and readiness (66.77 and 16.69, respectively; P=.002), while first-level students had the lowest (60.59 and 15.15). Among course groups, students enrolled in Maternal and Child Health Nursing reported the highest mean scores (65.19 and 16.30), whereas those in Community Health Nursing reported the lowest (57.50 and 14.38; P<.001). Conclusions: The findings indicate that nursing students demonstrated a generally positive level of readiness and acceptance toward the use of AI and related technologies in education and training. However, these levels remained moderate overall, highlighting the need to enhance awareness and deepen students’ understanding of AI’s potential to improve training effectiveness and health care quality.

Nurses in neonatal intensive care units face critical challenges in managing emergencies where timely, accurate interventions are essential for survival. Traditional nursing education often lacks the hands-on, immersive training required to build complex emergency skills, contributing to persistent neonatal mortality globally. Virtual reality (VR) simulation, grounded in Kolb’s Experiential Learning Theory, offers a promising solution by replicating realistic and repeatable clinical scenarios. While VR has shown potential in nursing education, its specific impact on high-risk neonatal emergencies remains underexplored. To evaluate the effectiveness of a VR simulation program in enhancing nurse competency and improving neonatal outcomes during emergency care, grounded in Kolb’s Experiential Learning Theory. A concurrent triangulation mixed-methods design was implemented over two weeks across four pediatric hospitals. Through stratified random sampling, 128 NICU nurses were allocated to VR simulation (n = 64) or traditional training (n = 64) groups. Quantitative data were collected using validated instruments (OSCE: CVI = 0.92, MCQ: α = 0.86) measuring clinical skills, knowledge retention, and decision-making accuracy. Qualitative data were gathered through semi-structured interviews (n = 24) exploring experiential aspects. The VR group showed significant improvements in clinical skills (OSCE: +16.1 points, p < 0.001, d = 1.58), decision-making accuracy (+ 16.7%, p < 0.001), and reduced stabilization times (-6.2 min, p < 0.001). Patient safety events decreased by 52% (p < 0.001). Thematic analysis revealed enhanced professional competence (83%), reduced clinical anxiety (75%), and positive learning experiences (88%), despite minor technical challenges. VR simulation demonstrates superior effectiveness for neonatal emergency training, significantly improving both nurse competency and patient outcomes. While geographic specificity and brief follow-up duration limit generalizability, findings support VR’s potential for enhancing emergency preparedness. Future research should address longitudinal outcomes and implementation across diverse healthcare settings. Not applicable.

This systematic review explores the intricate relationship between environmental sustainability and infection control in healthcare. A comprehensive examination of twelve selected studies reveals key themes, including the pivotal role of leadership in motivating sustainable practices within healthcare facilities and the integration of sustainable design strategies for effective infection prevention and control. The review emphasizes the significance of strong leadership in driving a culture of environmental responsibility, fostering staff engagement, and ensuring the success of sustainability initiatives. Sustainable design strategies, such as energy-efficient building practices and eco-friendly materials, emerge as essential components of both environmental sustainability and infection control. By creating synergy between these objectives, healthcare facilities can simultaneously reduce their environmental footprint and enhance patient safety. This study underscores the importance of leadership commitment and sustainable design in shaping the future of healthcare toward a more environmentally responsible and infection-resistant paradigm.