PURPOSE: The purpose of this study was to examine the relationship between sleep quality and stress among nursing students. METHODS: A cross-sectional study was conducted with 94 nursing students from a University in Seoul. Participants completed questionnaires and the data were analyzed using t-test, ANOVA, and Pearson correlation coefficients. RESULTS: The mean score of sleep quality was 6.93±2.66 among nursing students and 81.9% had a sleep problem. The mean score of stress was 18.61±4.84. Sleep quality was significantly different by clinical practice days per week, subjective physical health status, and subjective mental health status. Stress levels were significantly different by subjective physical health status, subjective mental health status, social relationship satisfaction, and satisfaction levels of nursing major and university. A significant relationship between sleep quality and stress (r=.45, p < .001) was found, meaning that a lower quality of sleep was significantly correlated with higher stress level. CONCLUSION: The results indicated that most nursing students had sleep problems and stress. Therefore, interventions are needed to be developed to lower the level of stress and increase the quality of sleep among nursing students.
Cross-Sectional Studies ; Humans ; Korea ; Mental Health ; Nursing ; Seoul ; Students, Nursing

Background: This study aimed to validate questionnaires on adherence to physical distancing and health beliefs about coronavirus disease 2019 (COVID-19) among patients with cancer and explore their interaction with depression or viral anxiety among them. Methods: Through an online survey, data from 154 cancer patients (female: 82.5%, breast cancer: 66.2%, current cancer treatment, presence: 65.6%) were collected from March to June 2022. The survey gathered responses to questionnaires on adherence to physical distancing, health beliefs about COVID-19, perceived social norms, Stress and Anxiety to Viral Epidemics-6 items, and Patient Health Questionnaire-2. Confirmatory factor analysis (CFA) for construct validity and structural equation model (SEM) were performed. Results: The CFA showed a good model fit for adherence to physical distancing (comparative fit index [CFI] = 1.000, Tucker–Lewis index [TLI] = 0.930, root-mean-square-error of approximation [RMSEA] = 0.000, and standardized root-mean-square residual [SRMR] = 0.050) and a satisfactory model fit for health beliefs about COVID-19 (CFI = 0.978, TLI = 0.971, RMSEA = 0.061, and SRMR = 0.089). Through SEM, we found that personal injunctive norms were the main mediators linking health beliefs with physical distancing in patients with cancer. Depression also mediated the effects of viral anxiety and perceived severity on physical distancing (χ2 = 20.073, df = 15, P = 0.169; CFI = 0.984; RMSEA = 0.047). Conclusion The questionnaires are reliable and valid. Patients with cancer may be able to adhere to physical distancing by addressing perceived severity, viral anxiety, perceived benefits, self-efficacy, perceived barriers, as well as personal injunctive norms.

Severe acute respiratory distress syndrome (ARDS) is a life-threatening disease with a high mortality rate. Although many therapeutic trials have been performed for improving the mortality of severe ARDS, limited strategies have demonstrated better outcomes. Recently, advanced rescue therapies such as extracorporeal membrane oxygenation (ECMO) made it possible to consider lung transplantation (LTPL) in patients with ARDS, but data is insufficient. We report a 62-year-old man who underwent LTPL due to ARDS with no underlying lung disease. He was admitted to the hospital due to influenza A pneumonia-induced ARDS. Although he was supported by ECMO, he progressively deteriorated. We judged that his lungs were irreversibly damaged and decided he needed to undergo LTPL. Finally, bilateral sequential double-lung transplantation was successfully performed. He has since been alive for three years. Conclusively, we demonstrate that LTPL can be a therapeutic option in patients with severe ARDS refractory to conventional therapies.
Extracorporeal Membrane Oxygenation ; Humans ; Influenza, Human* ; Lung Diseases ; Lung Transplantation* ; Lung* ; Middle Aged ; Mortality ; Pneumonia* ; Respiratory Distress Syndrome, Adult*

BACKGROUND: The role of antidepressants (ADs) in bipolar disorder is long-standing controversial issue in psychiatry. Many clinicians have used ADs as a treatment for bipolar depression, and the selection of therapeutic agents is very diverse and inconsistent. This study aimed to examine recent AD prescription patterns for patients with bipolar disorder in Korea, using the nationwide, population-based data. METHODS: This study utilized the Korean nationwide, whole population-based registry data of the year 2010, 2011, and 2013. All prescription data of the ADs, antipsychotics, and mood stabilizers of the sampled patients diagnosed with bipolar disorder (n = 2,022 [in 2010]; 2,038 [in 2011]; 2,626 [in 2013]) were analyzed for each year. RESULTS: Annual prescription rate of ADs was 27.3%–33.6% in bipolar disorder, which was gradually increasing over the 3-year period. The combination pattern of ADs and antipsychotic drugs tended to increase over 3 years. The proportion of females and the prevalence of comorbid anxiety disorder were significantly higher in AD user group in all three years. Among individual ADs, escitalopram was prescribed most frequently, and fluoxetine and bupropion were prescribed to the next many patients. The mean duration of bipolar depressive episodes was 135.90–152.53 days, of which ADs were prescribed for 115.60–121.98 days. CONCLUSION: Our results show prescription rate of ADs in bipolar disorder was maintained at substantial level and increased in recent 3 years. More empirical data and evidence are needed to establish practical treatment consensuses.
Antidepressive Agents ; Antipsychotic Agents ; Anxiety Disorders ; Bipolar Disorder* ; Bupropion ; Citalopram ; Consensus ; Female ; Fluoxetine ; Humans ; Korea* ; Prescriptions* ; Prevalence

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objective: We aimed to explore whether nursing professionals’ psychological states affect their grief response for a patient’s death in the coronavirus disease-2019 (COVID-19) inpatients’ ward. Methods: Survey was conducted among frontline nursing professionals working in COVID-19 inpatients wards at three tertiary-level affiliated hospitals of the University of Ulsan during April 7–26, 2022. Participants’ information such as age, years of employment, or marital status were collected, and their responses to rating scales including Pandemic Grief Scale (PGS) for healthcare workers, Stress and Anxiety to Viral Epidemics-9 items (SAVE-9), Patient Health Questionnaire-9 (PHQ-9), Loneliness and Social Isolation Scale, and Insomnia Severity Scale (ISI) were collected. Results: All 251 responses were analyzed. We observed that 34% reportedly suffered from depression. The linear regression analysis showed that a high PGS score was expected by high SAVE-9 (β=0.12, p=0.040), high PHQ-9 (β=0.25, p<0.001), high loneliness (β=0.17, p=0.006), and high ISI score (β=0.16, p=0.006, F=20.05, p<0.001). The mediation analysis showed that the depression of nursing professionals directly influenced their pandemic grief reaction, and their work-related stress and viral anxiety, insomnia severity, and loneliness partially mediated the association. Conclusion We confirm that frontline nursing professionals’ depression directly influenced their grief reaction, and their work-related stress and viral anxiety, insomnia severity, and loneliness partially mediated the association. We hope to establish a psychological and social support system for the mental health of nurses working in the COVID-19 wards.

Background: This study investigated the relationship between preoccupation with coronavirus disease 2019 (COVID-19), reassurance-seeking behavior, viral anxiety, intolerance of uncertainty, and adherence to physical distancing among frontline nursing professionals working in COVID-19 inpatient wards. Additionally, the study aimed to determine whether the commitment to physical distancing mediates the influence of intolerance of uncertainty on viral anxiety. Methods: Frontline healthcare professionals working in the COVID-19 inpatient wards at three tertiary-level affiliated hospitals in Korea were surveyed between April 7 and 26, 2022.The survey included scales—such as the Obsession with COVID-19 Scale, Coronavirus Reassurance-Seeking Behaviors Scale, Fear of COVID-19 Scale, and Intolerance of Uncertainty Scale-12 and a questionnaire on adherence to physical distancing. A total of 256 responses were analyzed after excluding inappropriate or incomplete responses. Results: Pearson’s correlation analysis found that age was significantly associated with the Obsession with COVID-19 Scale (r = −0.12, P < 0.05) and adherence to physical distancing (r = 0.27, P < 0.01). Linear regression analysis ascertained that age (β = −0.07, P = 0.002), Coronavirus Reassurance-Seeking Behaviors Scale (β = 0.35, P < 0.001), and Fear of COVID-19 Scale (β = 0.24, P < 0.001) were predictors of obsession with COVID-19 (Adjusted R 2 = 0.60, F = 78.1, P < 0.001). The indirect pathway by mediation analysis showed that reassurance-seeking and viral anxiety mediated the effect of intolerance of uncertainty on the preoccupation with COVID-19. Conclusion During the pandemic, there may be a strong association between reassuranceseeking behavior, viral anxiety, and a heightened preoccupation with COVID-19 among frontline healthcare workers. Thus, from the early stages of infectious disease, a psychological support team for medical staff responding to the disease should be established, and periodic evaluations should be conducted to identify high-risk groups.