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This study used text network analysis and topic modeling to examine the knowledge structure of family-centered care in neonatal ICU nurses. Text was extracted from abstracts of 110 peer-reviewed articles published between 1995 and 2023 and analyzed by identifying keywords, topics, and changes in research topics over time. Analysis of keywords revealed significant terms including “infant,” “family,” “experience,” “interventions,” and “parent participation,” highlighting family's central roles in family-centered care in neonatal ICU discourse. The research topics identified included “family-centered partnerships,” “barriers to implementing family-centered care,” “infant-mother attachment intervention,” “family participation intervention,” and “parenthood.” Over time, research on family-centered care in neonatal ICUs nurses has steadily increased, with notable increases in “family-centered partnerships” and “barriers to implementing family-centered care.” The findings underscore the evolving landscape of family-centered care in neonatal ICUs, emphasizing the critical role of collaborative care models in enhancing neonatal and familial outcomes. These insights provide a foundation for developing family-centered care programs that empower both nurses and families, supporting the holistic care of vulnerable infants. This study's results offer comprehensive insights into understanding family-centered care in the neonatal ICUs and could serve as a foundation for future studies to develop family-centered care programs for neonatal ICU nurses and families. Based on this study, it is recommended that nursing education programs integrate family-centered care training into their curricula, with an emphasis on communication, cultural competence, and family partnerships. Dashboards display hospital quality and patient safety measures aimed to improve patient outcomes. Although literature establishes dashboards aid quality and performance improvement initiatives, research is limited from the frontline nurse manager's perspective. This study characterizes factors influencing hospital nurse managers' use of dashboards for unit-level quality and performance improvement with suggestions for dashboard design. Using a descriptive qualitative design, semistructured interviews were conducted with 11 hospital nurse managers from a health system in the Midwestern United States. Thematic analysis was used to describe four perceived factors influencing dashboard use: external, data, technology features, and personal. External factors included regulatory standards, professional standards of care, organizational expectations, and organizational resources. Data factors included dashboard data quality and usefulness. Technology features included preference for simple, interactive, and customizable visual displays. Personal factors included inherent nurse manager qualities and knowledge. Guidelines for dashboard design involve display of required relevant quality measures that are accurate, timely, useful, and usable. Future research should involve hospital nurse managers in user-centered design to ensure dashboards are favorable for use. Further, opportunities exist for nurse manager informatics training and education on dashboard use in preparation for their role and responsibilities in unit-level quality and performance improvement. Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical decision support system in postembolization syndrome management across nurses and patient outcomes. This study is a randomized controlled trial. We included 40 RNs and 51 hospitalized patients in the study. For nurses in the experimental group, a clinical decision support system and a handbook were provided for 6 weeks, and for nurses in the control group, only a handbook was provided. Notably, the experimental group exhibited statistically significant improvements in patient-centered caring attitude, pain management barrier identification, and comfort care competence after clinical decision support system implementation. Moreover, patients' symptom interference during the experimental period significantly decreased compared with before the intervention. This study offers insights into the potential of clinical decision support system in refining nursing practices and nurturing patient well-being, presenting prospects for advancing patient-centered care and nursing competence. The clinical decision support system contents, encompassing postembolization syndrome risk prediction and care recommendations, should underscore its role in fostering a patient-centered care attitude and bolster nurses' comfort care competence. This study aimed to examine the effectiveness of an infection control simulation using mixed reality, comparing simulation fidelity with a high-fidelity mannequin (MN) group and problem-based learning with written cases group. This study used a three-group pretest-posttest quasi-experimental design. Two universities with similar curricula were conveniently selected, and a total of 72 nursing students were recruited. Participants were randomly assigned to three groups of 24 each. In the final analysis, there were 22 participants in the mixed reality groups, 21 in the mannequin groups, and 23 in the problem-based learning with written cases groups. Data were analyzed using descriptive statistics and the χ2, Kruskal-Wallis, and Wilcoxon signed rank tests. The mixed reality groups had a significantly positive effect on clinical reasoning ability and clinical competence than the problem-based learning with written cases groups, whereas the mannequin groups had a significantly positive effect on clinical competence than the problem-based learning with written cases groups. Mixed reality simulation is an appropriate simulation method that enhances learning immersion, satisfaction, and self-confidence in simulation. Additionally, it can substitute for mannequin simulation in terms of clinical reasoning ability and clinical competence. This study suggests that it is important to the various approaches in simulation fidelity to diversely enhance the competency of nursing students in simulation outcomes. Family-centered care is an approach to promote the health and well-being of children with chronic diseases and their families. This study aims to explore the knowledge components, structures, and research trends related to family-centered care for children with chronic conditions. We conducted the keyword network analysis in three stages using the keywords provided by the authors of each study: (1) search and screening of relevant studies, (2) keyword extraction and refinement, and (3) data analysis and visualization. The core keywords were child, adolescence, parent, and disabled. Four cohesive subgroups were identified through degree centrality. Research trends in the three phases of a recent decade have been changed. With the systematic understanding of the context of the knowledge structure, the future research and effective strategy establishment are suggested based on family-centered care for children with chronic disease. As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has not been established. This study aimed to develop a model for identifying predictors of survival over time in patients with out-of-hospital cardiac arrest during their stay in the emergency department, using ensemble-based machine learning. A total of 26 013 patients from the Korean nationwide out-of-hospital cardiac arrest registry were enrolled between January 1 and December 31, 2019. Our model, comprising 38 variables, was developed using the Survival Quilts model to improve predictive performance. We found that changes in important variables of patients with out-of-hospital cardiac arrest were observed 10 minutes after arrival at the emergency department. The important score of the predictors showed that the influence of patient age decreased, moving from the highest rank to the fifth. In contrast, the significance of reperfusion attempts increased, moving from the fourth to the highest rank. Our research suggests that the ensemble-based machine learning model, particularly the Survival Quilts, offers a promising approach for predicting survival in patients with out-of-hospital cardiac arrest. The Survival Quilts model may potentially assist emergency department staff in making informed decisions quickly, reducing preventable deaths. New nurses must acquire accurate knowledge of medication administration, as it directly affects patient safety. This study aimed to develop a microlearning-based self-directed learning chatbot on medication administration for novice nurses. Furthermore, the study had the objective of evaluating the chatbot feasibility. The chatbot covered two main topics: medication administration processes and drug-specific management, along with 21 subtopics. Fifty-eight newly hired nurses on standby were asked to use the chatbot over a 2-week period. Moreover, we evaluated the chatbot's feasibility through a survey that gauged changes in their confidence in medication administration knowledge, intrinsic learning motivation, satisfaction with the chatbot's learning content, and usability. After using the chatbot, participants' confidence in medication administration knowledge significantly improved in all topics (P There is an increasing need for highly accessible health management platforms for comprehensive symptoms of Parkinson disease. Mobile apps encompassing nonmotor symptoms have been rarely developed since these symptoms are often subjective and difficult to reflect what individuals actually experience. The study developed an app for comprehensive symptom management and evaluated its usability and feasibility. A single-group repeated measurement experimental design was used. Twenty-two participants used the app for 6 weeks. Monitoring of nonmotor symptoms, games to address motor symptoms, and medication management were incorporated in the app. Quantitative outcomes were self-assessed through an online questionnaire, and one-on-one telephone interviews were conducted to understand the user's point of view. The successful experience of self-monitoring had improved participants' self-efficacy (Z = −3.634, P No abstract available This study aimed to develop a Mobile Application to Prevent Recurrent Stroke to prevent recurrent stroke by enhancing self-management and to evaluate its effects on stroke survivors' health outcomes. The Mobile Application to Prevent Recurrent Stroke was developed based on social cognitive theory and the model in order of analysis, design, development, implementation, and evaluation process. The Mobile Application to Prevent Recurrent Stroke consisted of health management contents such as information about stroke, its associated risk factors, and required skills to conduct self-management with tailored support and counseling. A quasi-experimental preintervention and postintervention design was used involving a total of 54 stroke survivors. The experimental group (n = 27) was provided the Mobile Application to Prevent Recurrent Stroke for 8 weeks, whereas the control group (n = 27) received an education booklet. The result revealed that medication adherence (P = .002), healthy eating habit (P An effective prebriefing strategy is needed that can improve the learning outcomes of nurses in advanced life support education. This study aimed to identify the effects of prebriefing with online team-based learning on hospital nurses' knowledge, performance, and self-efficacy in advanced life support education. A nonequivalent control group pretest-posttest design was adopted. Nurses in the experimental group (n = 26) participated in prebriefing using online team-based learning followed by self-directed learning, whereas nurses in the control group (n = 27) experienced only self-directed learning before advanced life support education. Wilcoxon signed-ranks tests were used to identify the posttest-pretest differences of the study variables in each group. Both groups showed improved knowledge, individual performance, and self-efficacy after the education. Nurses in the experimental group reported higher self-efficacy scores compared with those in the control group. There were no differences between the experimental and control groups in knowledge, individual performances, or team performance. Online team-based learning as a prebriefing modality resulted in greater improvements in self-efficacy in advanced life support education. 
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