Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning

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.

Identifying Main Themes in Diabetes Management Interviews Using Natural Language Processing–Based Text Mining

This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program experience and satisfaction. Semistructured interview questions were used, and the interviews were auto-recorded. The interview transcripts were preprocessed and analyzed using four natural language processing–based text-mining techniques. The top 30 words from the term frequency and term frequency–inverse document frequency each were derived. In the N-gram analysis, the connection strength of “diabetes” and “education” was the highest, and the simultaneous connectivity of word chains ranged from a maximum of seven words to a minimum of two words. Based on the CONvergence of iteration CORrelation (CONCOR) analysis, three clusters were generated, and each cluster was named as follows: participation in a diabetes education program to control blood glucose, exercise, and use of digital devices. This study using text mining proposes a new and useful approach to visualize data to develop patient-centered diabetes education.

Digital Literacy and Associated Factors in Older Adults Living in Urban South Korea: A Qualitative Study

This study aimed to explore digital literacy among community-dwelling older adults in urban South Korea. A semistructured interview guide was developed using the Digital Competence ( 2.0 framework, which emphasizes the competencies for full digital participation in five categories: information and data literacy, communication and collaboration, content creation, safety, and problem-solving. The data were analyzed using combined inductive and deductive content analysis. Inductive analysis identified three main categories: perceived ability to use digital technology, responses to digital technology, and contextual factors. In the results of deductive analysis, participants reported varying abilities in using digital technologies for information and data literacy, communication or collaboration, and problem-solving. However, their abilities were limited in handling the safety or security of digital technology and lacked in creating digital content. Responses to digital technology contain subcategories of perception (positive or negative) and behavior (trying or avoidance). Regarding contextual factors, aging-related physical and cognitive changes were identified as barriers to digital literacy. The influence of families or peers was viewed as both a facilitator and a barrier. Our participants recognized the importance of using digital devices to keep up with the trend of digitalization, but their digital literacy was mostly limited to relatively simple levels.

Effect of Virtual Game–Based Integrated Clinical Practice Simulation Program on Undergraduate Nursing Students' Attitude Toward Learning

Game-based virtual reality simulation programs can capitalize on the advantages of non–face-to-face education while effectively stimulating the interest of trainees and improving training efficiency. This study aimed to develop a game-based virtual reality simulation program for nervous system assessment and to evaluate the effects of the program on the learning attitudes of nursing students. Using a one-group pretest-posttest design, 41 senior nursing students were enrolled, and their learning attitudes (self-directed learning attitude, academic self-efficacy, flow-learning experience, and learning presence) were evaluated. The effect of the program was statistically significant in self-directed learning attitude (t = −2.27, P = .027) and learning presence (t = −3.07, P = .003), but the difference was not statistically significant in academic self-efficacy (t = −1.97, P = .054) and learning flow (t = −0.74, P = .459). The virtual gaming simulation program can be used to effectively replace field training in situations wherein field training is limited, such as during the COVID-19 pandemic.

Identifying Latent Topics and Trends in Premature Infant–Related Nursing Studies Using a Latent Dirichlet Allocation Method

This study aimed to identify topics and within-topic core keywords in premature infant–related nursing studies published in Korean and international academic journals using topic modeling and to compare and analyze the trends in Korean and international studies. Journal databases were searched to extract nursing studies involving premature infants from 1998 to 2020. Journal databases included MEDLINE, Web of Science, CINAHL, and EMBASE for international studies and DBpia, the National Digital Science Library, the Korea Citation Index, and the Research Information Sharing Service for Korean studies. Abstracts from the selected 182 Korean and 2502 international studies were analyzed using NetMiner4.4.3e. In results, four similar topics (Korean vs international) were “pain intervention” versus “pain management”; “breast feeding practice” versus “breast feeding”; “kangaroo mother care”; and “parental stress” versus “stress & depression.” Two topics that appeared only in the international studies were “infection management” and “oral feeding & respiratory care.” Overall, the international studies dealt with diverse topics directly associated with premature. Korean studies mainly dealt with topics related to mothers of premature infants, whereas studies related to premature infants were insufficient. Nursing research in Korea needs to be expanded to research topics addressing premature infants.