Text Network Analysis of Research Topics and Trends on Simulations Using Virtual Patients in Nursing Education.

Simulations using virtual patients have been utilized as an effective method in nursing education. However, keyword network analyses and topic modeling on simulations using virtual patients in nursing education have not yet been performed. In this study, 213 articles were retrieved from online research article databases. Abstracts from these articles were extracted, and network analysis was conducted using NetMiner version 4.3 (Cyram Inc, Seongnam, South Korea). Based on the study's analysis, scenario, communication, system, assessment, person, disaster, and management were identified as the keywords with high centrality values. Therefore, they were determined to be influential in the network. After topic modeling, 10 topics were derived as dementia care competency, pain assessment, airway placement management, operating procedure, presence and satisfaction, communication and attitude improvement, platform world, disaster response, game and video usability, and system for confidence. The identified trends in this study will help grasp the trends and insight to guide future research directions on simulations using virtual patients in nursing education.

The Effect of Nursing Simulation on the Clinical Judgment of Nursing Care for Patients with Increased Intracranial Pressure (IICP).

BACKGROUND: Nursing educators commonly adopt simulations to educate nursing students and evaluate their clinical thinking, clinical reasoning, and clinical judgment. We aimed to determine the effectiveness of established nursing care simulations by evaluating, through video, a select number of nursing students in scenarios that simulate nursing care for Increased Intracranial Pressure (IICP) patients. METHODS: The participants were students in their senior year at a nursing college in South Korea in 2018. We adopted a mixed-method design by first conducting a nonequivalent control group pre-test/post-test research design, then analyzing the experimental group's simulation videos. The participants consisted of 38 students in the experimental group and 39 students in the control group, and each group consisted of 9 teams. This study measured the level of anxiety (Cronbach's α=0.780), critical thinking (Cronbach's α=0.895), performance (Cronbach's α=0.927), theoretical knowledge (Cronbach's α=0.970), and analyzed clinical judgments by directly reviewing video from the experimental group. We used a mixed methods appraisal tool (MMAT) version 2018 as its research checklist. RESULTS: Confidence (P=0.000), theoretical knowledge (P=0.000), clinical performance (P=0.017) indicated statistically significant increases in the experimental group. We subsequently identified 10 clinical judgment processes, including "Identify the patient's condition" to "Reassess the condition after symptoms improve" by analyzing the simulation videos. CONCLUSION: Developing a simulation for nursing care is effective in honing students' clinical judgment and enhancing their theoretical knowledge, confidence, and clinical performance.

The Performance of Healthcare-associated Infection Control Guideline among Hospital Nurses: A Structural Equation Model.

BACKGROUND: To improve efficient and systematic management and following of healthcare-associated infection guidelines, the relationship among various factors must be understood. Efforts should be made to strengthen or reduce relevant factors. We developed a structural equation model for following HAI-control guidelines. METHODS: Overall, 388 Korean registered nurses participated in this study and data collection was conducted Jul 21-Aug 31, 2014 using a self-reported questionnaire, and 392 of 400 copies were collected (98% recovery rate). Data were analyzed using descriptive statistics, reliability, and Pearson's coefficient. Model identification evaluation was conducted by goodness-of-fit index and impact analysis. RESULTS: Results of goodness-of-fit of modified model were as follows: χ2=120.57 (df=16, P<.001), GFI (Goodness of Fit Index)=.95, RMSEA (Root-Mean-Square Error of Approximation)=.10, SRMR (Standardizes Root-Mean-Square residual)=.06, NFI (Normal Fir Index)=.90, CFI (Comparative Fit Index)=.90. Factors with a statistically significant direct impact on behavioral intention are as follows; perceived behavior control (ß=.35, P=.008), subjective norm (ß=.27, P=.002), and job demands (ß=-.08, P=.083). Organizational factors directly influenced following guidelines. Explanatory power of organizational factors on guideline compliance was greatest (C.R. [Critical Ratio] =5.67, P<.001). CONCLUSION: Supportive leadership and a positive organizational culture development strategy are essential. The hospital leader group should provide adequate resources to facilitate compliance with guidelines as well as create an atmosphere of on-site support that ensures guidelines will be followed, and engage in continuous monitoring and feedback regarding following of guidelines.

Active surveillance for carbapenem-resistant Acinetobacter baumannii in a medical intensive care unit: Can it predict and reduce subsequent infections and the use of colistin?

BACKGROUND: Infection caused by carbapenem-resistant Acinetobacter baumannii (CRAB) has become a major problem in intensive care units (ICUs), with high incidence and mortality. This prospective study investigated the diagnostic value and influence of active surveillance, followed by isolation and enhanced contact precaution (IECP), on the subsequent CRAB infection and colistin use. METHODS: The study prospectively enrolled 1,115 patients who were admitted to the medical ICU of Chonnam National University Hwasun Hospital between April 2011 and November 2014. Active surveillance cultures were obtained from the throat or trachea, skin, and urine. IECP was performed beginning April 2013. RESULTS: Active surveillance detected CRAB in 168 (15%) patients and CRAB infection developed in 70 (6%) patients. Endotracheal tube was independently associated with both CRAB colonization and infection, whereas IECP was inversely associated with both CRAB colonization and infection in multivariate analysis (all P values <.001). The sensitivity, specificity, and positive and negative predictive values of active surveillance for subsequent CRAB infection were 84%, 90%, 47%, and 98%, respectively. The rate of CRAB acquisition, CRAB infection, and the use of colistin were significantly lower during the IECP period compared with the control period (6.5 vs 34.1, 2.6 vs 14.7, and 19.9 vs 65.5 per 1,000 patient-days, respectively; all P <.001). CONCLUSIONS: Active surveillance has good specificity and negative predictive value for subsequent CRAB infection. Active surveillance followed by IECP was inversely associated with the acquisition of CRAB and subsequent CRAB infection, and was associated with a reduction in colistin use in ICU patients.