The aim of this study was to investigate comorbidities in patients with end-stage renal disease, and to compare health-related quality of life (HRQOL) according to the type, and number of comorbidities.

Purpose: The aim of this study was to identify the relationships between health literacy competencies and patient-centered care among clinical nurses. Methods: The participants of this study were 254 nurses working in two hospitals in the D region. The data were collected from July to August 2020. The health literacy competencies for registered nurses scale and individualized care scale were utilized. Descriptive statistics, independent t-test, ANOVA, Pearson’s correlation coefficient and multiple regression analysis were used for data analysis. Results: The mean of health literacy competencies was 3.16±0.31 points on a four-point scale, and the average of patient-centered care was 3.69±0.50 points on a five-point scale. Regarding the nurses’ general characteristics, patient-centered care showed significant differences according to age (F=4.68, p=.010), marital status (t=-2.38, p=.018), religion (F=3.03, p=.030), total clinical experience (F=2.94, p=.021) and prior health literacy knowledge (t=3.20, p=.002). As a result of a hierarchical multiple regression analysis, health literacy competencies (β=.63) were found to significantly influence patient-centered care. The explanatory power of the model was 41.0% (F=25.58, p<.001). Conclusion The study suggests that nurse’s health literacy competencies should be developed in order to improve patient-centered care. Nursing education should include an emphasis on integrating health literacy into the nursing school curriculum.

Purpose: The aim of this study was to identify the relationships between health literacy competencies and patient-centered care among clinical nurses. Methods: The participants of this study were 254 nurses working in two hospitals in the D region. The data were collected from July to August 2020. The health literacy competencies for registered nurses scale and individualized care scale were utilized. Descriptive statistics, independent t-test, ANOVA, Pearson’s correlation coefficient and multiple regression analysis were used for data analysis. Results: The mean of health literacy competencies was 3.16±0.31 points on a four-point scale, and the average of patient-centered care was 3.69±0.50 points on a five-point scale. Regarding the nurses’ general characteristics, patient-centered care showed significant differences according to age (F=4.68, p=.010), marital status (t=-2.38, p=.018), religion (F=3.03, p=.030), total clinical experience (F=2.94, p=.021) and prior health literacy knowledge (t=3.20, p=.002). As a result of a hierarchical multiple regression analysis, health literacy competencies (β=.63) were found to significantly influence patient-centered care. The explanatory power of the model was 41.0% (F=25.58, p<.001). Conclusion The study suggests that nurse’s health literacy competencies should be developed in order to improve patient-centered care. Nursing education should include an emphasis on integrating health literacy into the nursing school curriculum.

PURPOSE: The purpose of this study was to construct and test a hypothetical model of self-management in patients with hemodialysis based on the Self-Regulation Model and resource-coping perspective. METHODS: Data were collected from 215 adults receiving hemodialysis in 17 local clinics and one tertiary hospital in 2016. The Hemodialysis Self-management Instrument, the Revised Illness Perception Questionnaire, Herth Hope Index and Multidimensional Scale of Perceived Social Support were used. The exogenous variable was social context; the endogenous variables were cognitive illness representation, hope, self-management behavior, and illness outcome. For data analysis, descriptive statistics, Pearson correlation analysis, factor analysis, and structural equation modeling were performed. RESULTS: The hypothetical model with six paths showed a good fitness to the empirical data: GFI=.96, AGFI=.90, CFI=.95, RMSEA=.08, SRMR=.04. The factors that had an influence on self-management behavior were social context (β=.84), hope and cognitive illness representation (β=.37 and β=.27) explaining 92.4% of the variance. Self-management behavior mediated the relationship between psychosocial coping resources and illness outcome. CONCLUSION: This research specifies a more complete spectrum of the self-management process. It is important to recognize the array of clinical resources available to support patients' self-management. Healthcare providers can facilitate self-management through collaborative care and understanding the ideas and emotions that each patient has about the illness, and ultimately improve the health outcomes. This framework can be used to guide self-management intervention development and assure effective clinical assessment.
Adult ; Chronic Disease ; Health Behavior ; Health Personnel ; Health Resources ; Hope ; Humans ; Renal Dialysis* ; Self Care* ; Self-Control ; Statistics as Topic ; Tertiary Care Centers

PURPOSE: The purpose of this study was to identify the correlation among hope, physical symptoms, depressive mood, and quality of life and to examine the influence of hope on quality of life in young adults and patients in their prime who are on hemodialysis. METHODS: A secondary analysis using survey data was performed for 100 patients from 20-55 years of age treated in 10 local hemodialysis clinics. To measure hope, physical symptoms, depressive mood, and quality of life, Herth Hope Index, revised symptom scale, Hospital Anxiety and Depression Scale, and Satisfaction with Life Scale were utilized. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson correlation coefficient, and multiple regression. RESULTS: There were differences in the scores for hope according to income, job, and religion. Statistically significant relationships were found between hope and depressive mood, and between hope and quality of life. Hope predicted quality of life with the explanatory power of 43.4%. CONCLUSION: Results indicate that hope is a protective factor which has the potential to provide a clinically useful approach to helping patients with hemodialysis, especially, in young adults and patients in their prime. Interventions that support and facilitate hope need to be developed and tested.
Anxiety ; Depression* ; Hope* ; Humans ; Quality of Life* ; Renal Dialysis* ; Young Adult*

Purpose: The purpose of this study was to identify the relationships between resilience, social support, and body image in patients in an acute stage following traffic accidents and to investigate factors affecting body image. Methods: Data were collected from 86 patients at local hospitals from January 2019 to February 2020. To measure the variables, the body image scale, Conner-Davidson Resilience Scale, and Multidimensional Scale of Perceived Social Support were used. Data were analyzed using descriptive statistics, independent t-tests, analysis of variance, Pearson correlation coefficient, and multiple regression. Results: Mean age of participants was 43.40±14.75 and the proportion of men was 73.3%. The average score for resilience, social support, and body image were 65.16±16.89, 72.93±8.11, and 12.24±6.63, respectively. The highest item on the body image scale was “Are you dissatisfied with the appearance of your scar?”. There were differences in scores for body image according to gender, age, and living status. Resilience (r=-.68, p<.001) and social support (r=-.65, p<.001) were negatively correlated with body image. In the regression model, resilience (β=-.41), social support (β=-.30), and gender (β=.22) accounted for 57.6% of the variance in body image. Conclusion Resilience and social support were identified in this study as significant factors protecting body image of patients during the acute stage following a traffic accident. It is recommended that psychosocial nursing interventions be conducted throughout the course of treatment.

Purpose: This study aimed to establish a basis for minimizing readmission of patients with heart failure by comparing their demographic, clinical, psychosocial, and behavioral characteristics based on the presence or absence of readmission. Methods: This retrospective descriptive study included 160 elderly patients with heart failure aged 60 years and older, who regularly visited the cardiovascular outpatient clinic in K hospital in Daegu.Data were collected from April to December 2021 using self-report questionnaires including the Lubben Social Network Scale, the Scale of Positive and Negative Experience, and European Heart Failure Self-care Behaviour 9-Item Tools, which were translated in the Korean context. IBM SPSS Statistics 25 was used for analysis, and descriptive statistics, χ2 test, Fisher’s exact test, t-test, and logistic regression analysis were conducted. Results: The factors that significantly affected the readmission of elderly patients with heart failure were social network and the type of medications taken. The total explanatory power of the regression model was 22.7%. Social network (OR=0.93, p=.037) and the type of medication taken were 4~5 (OR=4.80, p=.014) and more than 6 medications (OR=7.84, p=.037) had a significant impact on readmission. Conclusion Social network was the most influential factor for readmission. Further studies are needed to minimize readmission by analyzing additional factors that show significant differences.

Purpose: This study aimed to establish a basis for minimizing readmission of patients with heart failure by comparing their demographic, clinical, psychosocial, and behavioral characteristics based on the presence or absence of readmission. Methods: This retrospective descriptive study included 160 elderly patients with heart failure aged 60 years and older, who regularly visited the cardiovascular outpatient clinic in K hospital in Daegu.Data were collected from April to December 2021 using self-report questionnaires including the Lubben Social Network Scale, the Scale of Positive and Negative Experience, and European Heart Failure Self-care Behaviour 9-Item Tools, which were translated in the Korean context. IBM SPSS Statistics 25 was used for analysis, and descriptive statistics, χ2 test, Fisher’s exact test, t-test, and logistic regression analysis were conducted. Results: The factors that significantly affected the readmission of elderly patients with heart failure were social network and the type of medications taken. The total explanatory power of the regression model was 22.7%. Social network (OR=0.93, p=.037) and the type of medication taken were 4~5 (OR=4.80, p=.014) and more than 6 medications (OR=7.84, p=.037) had a significant impact on readmission. Conclusion Social network was the most influential factor for readmission. Further studies are needed to minimize readmission by analyzing additional factors that show significant differences.

Purpose: This study aimed to establish a basis for minimizing readmission of patients with heart failure by comparing their demographic, clinical, psychosocial, and behavioral characteristics based on the presence or absence of readmission. Methods: This retrospective descriptive study included 160 elderly patients with heart failure aged 60 years and older, who regularly visited the cardiovascular outpatient clinic in K hospital in Daegu.Data were collected from April to December 2021 using self-report questionnaires including the Lubben Social Network Scale, the Scale of Positive and Negative Experience, and European Heart Failure Self-care Behaviour 9-Item Tools, which were translated in the Korean context. IBM SPSS Statistics 25 was used for analysis, and descriptive statistics, χ2 test, Fisher’s exact test, t-test, and logistic regression analysis were conducted. Results: The factors that significantly affected the readmission of elderly patients with heart failure were social network and the type of medications taken. The total explanatory power of the regression model was 22.7%. Social network (OR=0.93, p=.037) and the type of medication taken were 4~5 (OR=4.80, p=.014) and more than 6 medications (OR=7.84, p=.037) had a significant impact on readmission. Conclusion Social network was the most influential factor for readmission. Further studies are needed to minimize readmission by analyzing additional factors that show significant differences.

Purpose: This study aimed to establish a basis for minimizing readmission of patients with heart failure by comparing their demographic, clinical, psychosocial, and behavioral characteristics based on the presence or absence of readmission. Methods: This retrospective descriptive study included 160 elderly patients with heart failure aged 60 years and older, who regularly visited the cardiovascular outpatient clinic in K hospital in Daegu.Data were collected from April to December 2021 using self-report questionnaires including the Lubben Social Network Scale, the Scale of Positive and Negative Experience, and European Heart Failure Self-care Behaviour 9-Item Tools, which were translated in the Korean context. IBM SPSS Statistics 25 was used for analysis, and descriptive statistics, χ2 test, Fisher’s exact test, t-test, and logistic regression analysis were conducted. Results: The factors that significantly affected the readmission of elderly patients with heart failure were social network and the type of medications taken. The total explanatory power of the regression model was 22.7%. Social network (OR=0.93, p=.037) and the type of medication taken were 4~5 (OR=4.80, p=.014) and more than 6 medications (OR=7.84, p=.037) had a significant impact on readmission. Conclusion Social network was the most influential factor for readmission. Further studies are needed to minimize readmission by analyzing additional factors that show significant differences.