Background: In general, simulation-based learning (SBL) has been a part of nursing education in the past two decades, though nursing educators are facing difficulties in evaluating its effectiveness in theory and practice. The aim of this review was to synthesize the research findings regarding the effects of SBL among nursing students from published scientific articles. Methods: This integrative review focused on articles published in English between 2016 and 2020, identified by using electronic databases such as Cochrane Library, PubMed, Medline (Ovid), SCOPUS, RISS, and Korea Med. Results: Fifteen articles were selected by a step-by-step process. Data were synthesized and effects of SBL methods were derived under four themes: ‘Knows A Self Com_p’ such as knowledge and skills; Attitude; Self (learning, efficacy, determination, competency, confidence, utilization, satisfaction, assessment); and Com(n) (competency, communication, and confidence) and P (perceptions and performance). Conclusion: The review results showed that SBL is the best method for teaching clinical practice. Article-focused simulations with simulators were more effective than classroom teaching, depending on the context, topic, and method. The overall results showed that SBL is a positive, safe and effective method for nursing students in clinical and simulation room settings to improve the skills and practice of client care.

This study reviewed the papers that studied the effect of simulation nursing education in the nursing field and examined the trend of simulation nursing education for nursing college students in Korea. Background: Simulation-based education started receiving attention as a pedagogical method in order to provide medical service of high quality in an ethical and safe environment. This has been of great importance during the coronavirus disease 2019 global pandemic. This literature review was conducted to suggest a direction for simulation-based nursing education in Korea. Methods: For literature searches, the authors used the following search terms in the Web of Science, CINAHL, Scopus, PubMed—‘utilization’, ‘simulation,’ ‘nursing student’, ‘nursing education’. A final search was conducted on 6 January 2021. The materials for this study were collected through literature searches according to the PRISMA guidelines. Results: 25 papers were selected as the final literature for analysis. The study was conducted for 48 percent of senior students in nursing college students in Korea (N = 12). High fidelity (HF) as the simulation type was 44 percent (N = 11). The simulation education subjects were composed of 52 percent adult health nursing (N = 13). According to educational goals described by Benzamine Bloom (1956), 90% in the psychomotor domain is considered a positive learning achievement. Conclusions: Effectiveness in the psychomotor domain through simulation-based training is correlated with expert nursing. It is essential to develop a systematic debriefing model and methods to evaluate performance and learning in the short- and long-term to expand the effectiveness of simulation-based education in nursing.

Disease severities are the outcomes of an inpatient visit classification that assigns a diagnostic related group, including risk of mortality and severity of illness. Although widely used in healthcare, the analysis of factors affecting disease severities has not been adequately studied. In this study, we analyze the relationships between demographics and chronic conditions and specify their influence on disease severities. Descriptive statistics are used to investigate the relationships and the prevalence of chronic conditions. To evaluate the influence of demographic factors and chronic conditions on disease severities, several multinomial logistic regression models are performed and prediction models for disease severities are conducted based on National Inpatient Sample data for 2016 provided by the Healthcare Cost and Utilization Project database in the United States. The rate of patients with a chronic illness is 88.9% and the rate of patients with more than two chronic conditions is 67.6%; further, the rate is 62.7% for females, 73.9% for males, and 90% for the elderly. A high level of disease severity commonly appears in patients with more than two chronic conditions, especially in the elderly. For patients without chronic conditions, disease severities show a lower or safe level, even in the elderly.

Background: It is necessary to engage in an appropriate level of physical activity to improve the prognoses of colorectal cancer patients, yet no guidelines currently exist. The goals of this systematic review are to determine the impact of levels of physical activity on the prognoses of colorectal cancer patients and to suggest recommended guidelines for levels of physical activity. Methods: This systematic review was conducted along PRISMA guidelines. Per the inclusion criteria, papers published in academic journals in English from 2010 to 2019 were selected. A literature search was performed on PubMed (Medline), and the results of the selected studies were qualitatively synthesized. Results: Of the 13 cohort studies included in this systematic review, most studies were conducted in the United States (N = 7). Immobility or low levels of physical activity adversely affected the prognoses of colorectal cancer patients. Contrarily, high levels of physical activity increased the survival rate in people with colorectal cancer. Conclusion: For colorectal cancer patients, a level of physical activity of 17.5 to 35 MET hours per week is strongly recommended, which has been shown to reduce mortality by approximately 30 to 40%. Patients with limited physical capacity should maintain a minimum level of physical activity (≥3.5 MET hours/week).

Background/Objectives: Hospital length of stay (LOS) is widely analyzed and serves as a benchmark for assessing changes during hospitalization. This study introduced a method to estimate patients’ LOS and highlighted the variations in LOS among individuals with or without multiple chronic conditions (MCCs) and across different levels of disease severity, using data from the 2016 National Inpatient Sample in the United States. Methods: To analyze the factors influencing LOS, a multinomial logistic regression model was employed, demonstrating its effectiveness in estimating and predicting expected LOS. Factors such as demographic characteristics, MCCs, and disease severity were strongly linked to LOS. Results: The overall prevalence of MCCs exceeded 66%, rising to over 90% among elderly patients and more than 88% among those with severe diseases. LOS distribution was primarily concentrated within the first month following admission: over 13% of patients were discharged within a day, over 85% within a week, and more than 99% within a month. Multinomial logistic regression analysis showed that LOS was significantly influenced by age, disease severity, and the presence of MCCs. Older patients, especially those with MCCs, had significantly longer LOSs compared to younger patients without MCCs. Conclusions: LOS tended to increase with age and higher disease severity, particularly in patients with MCCs. Multinomial logistic regression revealed that patients over 65 and those with high disease severity (severity score 4) had significantly longer LOS. Shorter LOS was more frequent among patients under 65 years old, those without MCC, and those with low disease severity, whereas longer LOS was commonly observed in patients with MCCs or high disease severity.

Background: In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors, as well as the temporal order and intensity among disease-disease associations. Objective: This study sought to investigate the overall patterns of the associations among diseases; network properties, such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors. Methods: We used National Health Insurance Service-National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which included the time series insurance information of 1 million out of 50 million Korean (approximately 2%) patients obtained between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses, making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties. Results: Our disease network contained 294 nodes and 3085 edges, a relative risk value of more than 4, and a false discovery rate-adjusted P value of <.001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories. Conclusions: We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future.