No abstract available.

PURPOSE: This study aimed to explore the influence of nurse work environment and patient safety culture in hospital on instances of missed nursing care in South Korea. METHODS: A cross-sectional design was used, in which a structured questionnaire was administered to 186 nurses working at a tertiary university hospital. Data were analyzed using descriptive statistics, t test or analysis of variance, Pearson correlation, and multiple regression analysis. RESULTS: Missed nursing care was found to be correlated with clinical career, nursing work environment, and patient safety culture. The regression model explained approximately 30.3% of missed nursing care. Meanwhile, staffing and resource adequacy (β = −.31, p = .001), nurse manager ability, leadership and support of nurses (β = −.26, p = .004), clinical career (β = −.21, p = .004), and perception on patient safety culture within unit (β = −.19, p = .041) were determined to be influencing factors on missed nursing care. CONCLUSION: This study has significance as it suggested that missed nursing care is affected by work environment factors within unit. This means that missed nursing care is a unit outcome affected by nurse work environment factors and patient safety culture. Therefore, missed nursing care can be managed through the implementation of interventions that promote a positive nursing work environment and patient safety culture.
Humans ; Korea* ; Leadership ; Nurse Administrators ; Nursing Care* ; Nursing* ; Patient Safety* ; Quality of Health Care

PURPOSE: The purpose of this study was to investigate the effects of emergency setting visitors' knowledge and behavior patterns in relation to prevention of respiratory tract infections. METHODS: A descriptive survey was used. The participants were visitors to the emergency service in ‘D’ general hospital in ‘D’ city, and the data were collected from July 1 to September 1, 2016. The collected data were analyzed using frequency analysis, t-test, ANOVA, Scheffé test, Pearson correlation coefficients, and multiple regression analysis. RESULTS: Factors influencing prevention of respiratory tract infections were visitors' education level, methods of dissemination of prevention information, and participants' knowledge with regard to preventive methods. The explanatory power was found to be 35% in the regression model. CONCLUSION: The findings indicate that visitors' education level, knowledge of infection prevention, and the dissemination of information regarding infection prevention by the hospital play an important role in the prevention of respiratory tract infections in emergency services in the hospital. These results highlight the need for a customized education program for prevention of respiratory tract infections in emergency settings. Programs should take into consideration the educational background of visitors, and provide them with appropriate information regarding infection prevention.
Education ; Emergencies* ; Emergency Service, Hospital* ; Hospitals, General ; Respiratory System* ; Respiratory Tract Infections*

Purpose: The purpose of this study was to explore cancer center nurses experience of infection control. Methods: From March 26 to 2019 to May 1, 2019 data were collected through focus group interviews and in-depth interviews. Data collected though interviews and field notes were analyzed using Colaizzi's phenomenological method. Participants were 12 registered nurses and 2 head nurses. Results: Fifteen themes emerged from four themes clusters. 1) Encountering barriers to infection control. 2) Infection control is still difficult. 3) Complaints about Infection Control Department exist. 4) Need to consider strategies for improving infection control. Conclusion The results of the study indicate that in order to facilitate infection control and increase the compliance rate of the guidelines, it is necessary to develop a program to increase the individual's competency associated with infection control and the elimination of negative factors. Further, mediation of external factors related to the hospital environment is necessary.

Patients with cancer experience psychological and social problems; in particular, older patients with cancer face many difficulties during the cancer treatment process owing to aging and underlying diseases. Furthermore, the lives of individuals may be impacted by the COVID-19 pandemic. Therefore, the purpose of this study is to describe the experiences of older patients with cancer during the cancer diagnosis and treatment process amid the COVID-19 pandemic. Methods: This study employed a qualitative, descriptive phenomenological approach to explore and analyze the experiences of the participants. The participants in this study consisted of patients aged over 65 who were diagnosed with cancer, and data were collected from May 4 to June 30, 2022 through in-depth individual interviews. The collected data were analyzed using Giorgi’s phenomenological analysis. Results: Participants were six older patients with cancer with an average age of 69.66 years. Five theme clusters and 15 themes were generated. The theme clusters were “psychological adaptation to cancer diagnosis,” “receiving social and medical support,” “difficulty in the treatment process,” “fear of the COVID-19 pandemic,” and “living through difficulties.” Conclusion: This study revealed that older patients with cancer demonstrated a unique psychology about how to accept the shock of the diagnosis and the resulting coping patterns and feelings of depression. Additionally, it was verified that older patients with cancer, susceptible to infections due to their advanced age and weakened immunity, also face an increased vulnerability to COVID-19. Therefore, a support system tailored to the characteristics of older patients with cancer should be established.

Background: and Purpose: Recognizing cognitive decline patterns in mild cognitive impairment (MCI) is crucial for early screening and preventive interventions. However, studies on the trajectory of individual cognitive functions in MCI are limited. Thus, the purpose of this study was to identify subtypes and stages of cognitive decline in MCI using a machine learning method. Methods: A total of 944 subjects consisting of those who were cognitively normal and those with MCI were enrolled. Fifteen neuropsychological tasks were used in the analysis.The optimal number of subtypes was determined based on the cross-validation information criterion. Fifteen stages of cognitive trajectory were estimated for each subtype. Results: The following three subtypes were identified: amnestic-verbal subtype, dysexecutive subtype, and amnestic-visual subtype. Of 723 (76.6%) subjects who had reached stage 1 at least, amnestic-verbal subtype accounted for the most (n=340, 47.0%), followed by dysexecutive subtype (n=253, 35.0%) and amnestic-visual subtype (n=130, 18%). The amnestic-verbal subtype had significantly more males (amnestic-verbal: 41.8%, dysexecutive: 31.2%, and amnestic-visual: 28.5%), younger subjects (amnestic-verbal: 72.01 years, dysexecutive: 74.43 years, and amnestic-visual: 75.06 years), higher educational years (amnestic-verbal: 11.06 years, dysexecutive: 9.53 years, and amnestic-visual: 9.79 years), lower Clinical Dementia Rating sum of boxes (amnestic-verbal: 1.40, dysexecutive: 1.61, and amnestic-visual: 1.71), and lower Korean-Instrumental Activities of Daily Living score (amnestic-verbal: 0.20, dysexecutive: 0.27, and amnestic-visual: 0.26). Conclusions Three types of MCIs were extracted using SuStaIn. Pathways of MCI deterioration could be different. The amnestic type could be bisected based on whether episodic verbal or visual memory is degraded first.

Background: and Purpose: Recognizing cognitive decline patterns in mild cognitive impairment (MCI) is crucial for early screening and preventive interventions. However, studies on the trajectory of individual cognitive functions in MCI are limited. Thus, the purpose of this study was to identify subtypes and stages of cognitive decline in MCI using a machine learning method. Methods: A total of 944 subjects consisting of those who were cognitively normal and those with MCI were enrolled. Fifteen neuropsychological tasks were used in the analysis.The optimal number of subtypes was determined based on the cross-validation information criterion. Fifteen stages of cognitive trajectory were estimated for each subtype. Results: The following three subtypes were identified: amnestic-verbal subtype, dysexecutive subtype, and amnestic-visual subtype. Of 723 (76.6%) subjects who had reached stage 1 at least, amnestic-verbal subtype accounted for the most (n=340, 47.0%), followed by dysexecutive subtype (n=253, 35.0%) and amnestic-visual subtype (n=130, 18%). The amnestic-verbal subtype had significantly more males (amnestic-verbal: 41.8%, dysexecutive: 31.2%, and amnestic-visual: 28.5%), younger subjects (amnestic-verbal: 72.01 years, dysexecutive: 74.43 years, and amnestic-visual: 75.06 years), higher educational years (amnestic-verbal: 11.06 years, dysexecutive: 9.53 years, and amnestic-visual: 9.79 years), lower Clinical Dementia Rating sum of boxes (amnestic-verbal: 1.40, dysexecutive: 1.61, and amnestic-visual: 1.71), and lower Korean-Instrumental Activities of Daily Living score (amnestic-verbal: 0.20, dysexecutive: 0.27, and amnestic-visual: 0.26). Conclusions Three types of MCIs were extracted using SuStaIn. Pathways of MCI deterioration could be different. The amnestic type could be bisected based on whether episodic verbal or visual memory is degraded first.

Background: and Purpose: Recognizing cognitive decline patterns in mild cognitive impairment (MCI) is crucial for early screening and preventive interventions. However, studies on the trajectory of individual cognitive functions in MCI are limited. Thus, the purpose of this study was to identify subtypes and stages of cognitive decline in MCI using a machine learning method. Methods: A total of 944 subjects consisting of those who were cognitively normal and those with MCI were enrolled. Fifteen neuropsychological tasks were used in the analysis.The optimal number of subtypes was determined based on the cross-validation information criterion. Fifteen stages of cognitive trajectory were estimated for each subtype. Results: The following three subtypes were identified: amnestic-verbal subtype, dysexecutive subtype, and amnestic-visual subtype. Of 723 (76.6%) subjects who had reached stage 1 at least, amnestic-verbal subtype accounted for the most (n=340, 47.0%), followed by dysexecutive subtype (n=253, 35.0%) and amnestic-visual subtype (n=130, 18%). The amnestic-verbal subtype had significantly more males (amnestic-verbal: 41.8%, dysexecutive: 31.2%, and amnestic-visual: 28.5%), younger subjects (amnestic-verbal: 72.01 years, dysexecutive: 74.43 years, and amnestic-visual: 75.06 years), higher educational years (amnestic-verbal: 11.06 years, dysexecutive: 9.53 years, and amnestic-visual: 9.79 years), lower Clinical Dementia Rating sum of boxes (amnestic-verbal: 1.40, dysexecutive: 1.61, and amnestic-visual: 1.71), and lower Korean-Instrumental Activities of Daily Living score (amnestic-verbal: 0.20, dysexecutive: 0.27, and amnestic-visual: 0.26). Conclusions Three types of MCIs were extracted using SuStaIn. Pathways of MCI deterioration could be different. The amnestic type could be bisected based on whether episodic verbal or visual memory is degraded first.

Background: and Purpose: Recognizing cognitive decline patterns in mild cognitive impairment (MCI) is crucial for early screening and preventive interventions. However, studies on the trajectory of individual cognitive functions in MCI are limited. Thus, the purpose of this study was to identify subtypes and stages of cognitive decline in MCI using a machine learning method. Methods: A total of 944 subjects consisting of those who were cognitively normal and those with MCI were enrolled. Fifteen neuropsychological tasks were used in the analysis.The optimal number of subtypes was determined based on the cross-validation information criterion. Fifteen stages of cognitive trajectory were estimated for each subtype. Results: The following three subtypes were identified: amnestic-verbal subtype, dysexecutive subtype, and amnestic-visual subtype. Of 723 (76.6%) subjects who had reached stage 1 at least, amnestic-verbal subtype accounted for the most (n=340, 47.0%), followed by dysexecutive subtype (n=253, 35.0%) and amnestic-visual subtype (n=130, 18%). The amnestic-verbal subtype had significantly more males (amnestic-verbal: 41.8%, dysexecutive: 31.2%, and amnestic-visual: 28.5%), younger subjects (amnestic-verbal: 72.01 years, dysexecutive: 74.43 years, and amnestic-visual: 75.06 years), higher educational years (amnestic-verbal: 11.06 years, dysexecutive: 9.53 years, and amnestic-visual: 9.79 years), lower Clinical Dementia Rating sum of boxes (amnestic-verbal: 1.40, dysexecutive: 1.61, and amnestic-visual: 1.71), and lower Korean-Instrumental Activities of Daily Living score (amnestic-verbal: 0.20, dysexecutive: 0.27, and amnestic-visual: 0.26). Conclusions Three types of MCIs were extracted using SuStaIn. Pathways of MCI deterioration could be different. The amnestic type could be bisected based on whether episodic verbal or visual memory is degraded first.

Purpose: The aim of this study was to identify factors affecting pressure ulcers among cognitive impairment inpatients with long term care facilities. Methods: In this secondary data analysis of the two long-term care facilities, 162 patients with mild or more cognitive impairment were analyzed. Patients with a pressure ulcer of stage 2 or higher that developed after the first day of hospitalization were defined as the pressure ulcer group, while the other patients comprised the non-pressure ulcer group. Demographic characteristics were compared between the two groups. Initially, a univariate analysis was conducted. Subsequently, risk factors were identified through logistic regression analysis using the variables that exhibited statistically significant differences in the univariate analysis. Results: Thirty-three patients had hospital-acquired pressure ulcers. Age (odds ratio [OR]=0.92, 95% confidence interval [CI]=0.87~0.98), albumin levels (OR=0.25, 95% CI=0.08~0.77), Charlson comorbidity index (OR=1.57, 95% CI=1.14~2.18), and Mini-Mental State Examination for Koreans score (OR=0.90, 95% CI=0.81~0.99) were identified as risk factors for pressure ulcer occurrence. Conclusion Our data analysis supports independent associations between certain clinical characteristics and pressure ulcer occurrence in long-term care facility patients with cognitive impairment. To reduce the risk of pressure ulcers in long-term care facilities, health professionals should implement appropriate prevention measures.