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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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