Purpose: This study examined the impact of professional Quality of life (QoL) on turnover intention among general hospital nurses using linear and nonlinear analytical techniques. Methods: Data were collected from 159 general hospital nurses and analyzed using SPSS, t-test, ANOVA, Pearson's correlation coefficients, multiple linear regression, and nonlinear machine learning models (Bootstrap Forest and Boosted Tree). Results: Significant correlations were observed between turnover intention and both compassion satisfaction (r=-.26, p<.001) and burnout (r=.27, p=.001). Compassion satisfaction, burnout, and compassion fatigue were identified as the key variables influencing turnover intention. The explanatory power of multiple linear regression analysis was 6.9%, whereas the nonlinear machine learning models demonstrated an explanatory power of 50.5% for Bootstrap Forest and 45.1% for Boosted Tree. Conclusion Continuous investment in human resource management, within nursing organizations, is essential to promote the long-term retention of general hospital nurses. This investment should focus on enhancing compassion satisfaction and reducing burnout and compassion fatigue by fostering a sense of vocation and positive job satisfaction.

Purpose: This study aimed to empirically verify the impact of measurement model selection on research outcomes and their interpretation through an analysis of children’s emotional and social problems measured by the Pediatric Symptom Checklist (PSC) using both reflective and formative measurement models. These models were represented by covariance-based structural equation modeling (CB-SEM) and partial least squares SEM (PLS-SEM), respectively. Methods: This secondary data analysis evaluated children’s emotional and social problems as both reflective and formative constructs. Reflective models were analyzed using CB-SEM, while formative models were assessed using PLS-SEM. Comparisons between these two approaches were based on model fit and parameter estimates. Results: In the CB-SEM analysis, which assumed a reflective measurement model, a model was not identified due to inadequate fit indices and a Heywood case, indicating improper model specification. In contrast, the PLS-SEM analysis, assuming a formative measurement model, demonstrated adequate reliability and validity with significant path coefficients, supporting the appropriateness of the formative model for the PSC. Conclusion The findings indicate that the PSC is more appropriately analyzed as a formative measurement model using PLS-SEM, rather than as a reflective model using CB-SEM. This study highlights the necessity of selecting an appropriate measurement model based on the theoretical and empirical characteristics of constructs in nursing research. Future research should ensure that the nature of measurement variables is accurately reflected in the choice of statistical models to improve the validity of research outcomes.

Purpose: This study examined the associations of grit, self-leadership, and followership with nurses’ competency in evidence-based practice in South Korea. Methods: This descriptive correlational study included 203 nurses from a general hospital. Data collection was conducted from May 8 to June 21, 2024. Results: The mean score for competency in evidence-based practice was 4.16 of 7. Competency in evidence-based practice correlated positively with grit (r = .62, p < .001), self-leadership (r = .54, p < .001), and followership (r = .72, p < .001). Followership (β = 0.53, p < .001), grit (β = 0.24, p < .001) and research experience (β = 0.11, p = .024) significantly influenced competency in evidence-based practice. The model’s explanatory power was calculated to be 55.1%. Conclusion Followership and grit are key factors in developing strategies to enhance nurses’ competency in evidence-based practice. Additionally, providing support for research experience in clinical settings may further strengthen competency in evidence-based practice.

Background: Factors influencing the decline in forced expiratory volume in one second (FEV1 )/forced vital capacity (FVC) for chronic obstructive pulmonary disease (COPD) progression remain uncertain. We aimed to identify risk factors associated with rapid FEV1 / FVC decline in patients with COPD. Methods: This multi-center observational study was conducted from January 2012 to December 2022. Eligible patients were monitored with symptoms, spirometric tests, and treatment patterns over 3 years. Rapid FEV1 /FVC decliners were defined as the quartile of patients exhibiting the highest annualized percentage decline in FEV1 /FVC. Results: Among 1,725 patients, 435 exhibited rapid FEV1 /FVC decline, with an annual change of −2.5%p (interquartile range, −3.5 to −2.0). Rapid FEV1 /FVC decliners exhibited lower body mass index (BMI), higher smoking rates, elevated post-bronchodilator (BD) FEV1 , higher post-BD FEV1 / FVC, and a lower prevalence of Staging of Airflow Obstruction by Ratio (STAR) stage IV. Rapid FEV1 /FVC decline was not linked to the annual exacerbation rate, but there was an association with symptom deterioration and FEV1 decline. In multivariable analyses, low BMI, current smoking, increased modified Medical Research Council dyspnoea score, low post-BD FEV1 , low STAR stage, high forced mid-expiratory flow (FEF 25-75% ), accelerated FEV1 decline, and not initiating dual BD therapy were identified as independent risk factors for rapid FEV1 /FVC decline. Conclusion We identified the risk factors for rapid FEV1 /FVC decline, including BMI, smoking, symptoms deterioration, FEV1 decline, and adherence to standard inhaler treatment. Our findings underscore the potential benefits of maintaining consistent use of long-acting beta-agonist/long-acting muscarinic antagonist even in the presence of worsening symptoms, in attenuating FEV1 /FVC decline.

Purpose: This study examined the impact of professional Quality of life (QoL) on turnover intention among general hospital nurses using linear and nonlinear analytical techniques. Methods: Data were collected from 159 general hospital nurses and analyzed using SPSS, t-test, ANOVA, Pearson's correlation coefficients, multiple linear regression, and nonlinear machine learning models (Bootstrap Forest and Boosted Tree). Results: Significant correlations were observed between turnover intention and both compassion satisfaction (r=-.26, p<.001) and burnout (r=.27, p=.001). Compassion satisfaction, burnout, and compassion fatigue were identified as the key variables influencing turnover intention. The explanatory power of multiple linear regression analysis was 6.9%, whereas the nonlinear machine learning models demonstrated an explanatory power of 50.5% for Bootstrap Forest and 45.1% for Boosted Tree. Conclusion Continuous investment in human resource management, within nursing organizations, is essential to promote the long-term retention of general hospital nurses. This investment should focus on enhancing compassion satisfaction and reducing burnout and compassion fatigue by fostering a sense of vocation and positive job satisfaction.

Purpose: This study aimed to empirically verify the impact of measurement model selection on research outcomes and their interpretation through an analysis of children’s emotional and social problems measured by the Pediatric Symptom Checklist (PSC) using both reflective and formative measurement models. These models were represented by covariance-based structural equation modeling (CB-SEM) and partial least squares SEM (PLS-SEM), respectively. Methods: This secondary data analysis evaluated children’s emotional and social problems as both reflective and formative constructs. Reflective models were analyzed using CB-SEM, while formative models were assessed using PLS-SEM. Comparisons between these two approaches were based on model fit and parameter estimates. Results: In the CB-SEM analysis, which assumed a reflective measurement model, a model was not identified due to inadequate fit indices and a Heywood case, indicating improper model specification. In contrast, the PLS-SEM analysis, assuming a formative measurement model, demonstrated adequate reliability and validity with significant path coefficients, supporting the appropriateness of the formative model for the PSC. Conclusion The findings indicate that the PSC is more appropriately analyzed as a formative measurement model using PLS-SEM, rather than as a reflective model using CB-SEM. This study highlights the necessity of selecting an appropriate measurement model based on the theoretical and empirical characteristics of constructs in nursing research. Future research should ensure that the nature of measurement variables is accurately reflected in the choice of statistical models to improve the validity of research outcomes.

Background and Objectives: SH3 and multiple ankyrin repeat domains 3 (Shank3) proteins play crucial roles as neuronal postsynaptic scaffolds. Alongside neuropsychiatric symptoms, individuals with SHANK3 mutations often exhibit symptoms related to dysfunctions in other organs, including the heart. However, detailed insights into the cardiac functions of Shank3 remain limited. This study aimed to characterize the cardiac phenotypes of Shank3-overexpressing transgenic mice and explore the underlying mechanisms. Methods: Cardiac histological analysis, electrocardiogram and echocardiogram recordings were conducted on Shank3-overexpressing transgenic mice. Electrophysiological properties, including action potentials and L-type Ca2+ channel (LTCC) currents, were measured in isolated cardiomyocytes. Ca2+ homeostasis was assessed by analyzing cytosolic Ca2+transients and sarcoplasmic reticulum Ca2+ contents. Depolarization-induced cell shortening was examined in cardiomyocytes. Immunoprecipitation followed by mass spectrometrybased identification was employed to identify proteins in the cardiac Shank3 interactome.Western blot and immunocytochemical analyses were conducted to identify changes in protein expression in Shank3-overexpressing transgenic cardiomyocytes. Results: The hearts of Shank3-overexpressing transgenic mice displayed reduced weight and increased fibrosis. In vivo, sudden cardiac death, arrhythmia, and contractility impairments were identified. Shank3-overexpressing transgenic cardiomyocytes showed prolonged action potential duration and increased LTCC current density. Cytosolic Ca2+ transients were increased with prolonged decay time, while sarcoplasmic reticulum Ca2+ contents remained normal. Cell shortening was augmented in Shank3-overexpressing transgenic cardiomyocytes. The cardiac Shank3 interactome comprised 78 proteins with various functions. Troponin I levels were down-regulated in Shank3-overexpressing transgenic cardiomyocytes. Conclusions This study revealed cardiac dysfunction in Shank3-overexpressing transgenic mice, potentially attributed to changes in Ca2+ homeostasis and contraction, with a notable reduction in troponin I.