Purpose: This study aimed to address the shift toward competency-based education and the planned 2028 “Integrated Nursing” National Licensing Examination (NLE), this study aimed to establish structural alignment among NLE domains, the seven integrated nursing competencies (INCs), and curriculum goals, with a particular focus on implementing symptom-based clinical reasoning (SBCR). Methods: This Delphi-based methodological study included seven content experts for content validity index (CVI) assessment and 24 nursing education experts who participated in a consensus workshop. The item-level CVI and the scale-level CVI/average were calculated to confirm the linkage between INCs and NLE domains. In addition, qualitative analysis of workshop materials and meeting records was conducted to derive 10 integrated learning topics and to develop an SBCR educational model for the key symptom of headache, grounded in Miller’s Clinical Competence Pyramid (levels 2–4). Results: The analysis confirmed the validity of integrating the INCs within the overall curriculum structure. The resulting framework delineates staged learning objectives and core clinical questions designed to systematically enhance clinical reasoning, promote safe nursing practice, and support professional reflection within a unified curriculum. Conclusion This study provides a practical foundation for nursing curriculum redesign by facilitating a transition from fragmented, subject-based instruction to a holistic, patient-centered SBCR model. This approach aligns with the requirements of the integrated NLE and is expected to contribute to meaningful improvements in actual clinical competency.

Purpose: This study aimed to evaluate surgical outcomes, identify complications, and analyze recurrence in patients undergoing surgery for primary retroperitoneal tumors (RPTs), which pose significant therapeutic challenges. Methods: We retrospectively reviewed the medical records of 59 patients who underwent surgery for primary RPTs at Ajou University Hospital between January 2015 and December 2024. Patients were divided into a multivisceral resection (MVR) group (n = 25) and a non-MVR group (n = 34) to compare demographics, clinical characteristics, pathological findings, and postoperative outcomes. Results: Compared to the non-MVR group, the MVR group had a significantly higher rate of open surgery (96.0% vs.73.5%, P = 0.034), longer operative times (237.6 minutes vs. 188.8 minutes, P = 0.032), more frequent R2 resections (32.0% vs. 8.8%, P = 0.018), and longer hospital stays (13.1 days vs. 7.0 days, P = 0.007). Malignant tumors comprised 67.7% of cases, with sarcoma being the most common. Recurrence rates were highest in well-differentiated liposarcoma (50.0%) and dedifferentiated liposarcoma (22.2%). While univariate analysis showed MVR was associated with severe morbidity (Clavien-Dindo grade ≥III; odds ratio, 6.200; P = 0.029), this was not sustained as an independent predictor in multivariable analysis. Conclusion Although MVR was associated with severe complications in univariate analysis, it was not an independent predictor in multivariable analysis. This suggests the increased risk reflects overall surgical complexity. Therefore, MVR should be pursued when oncologically necessary, as it presents an acceptable morbidity profile.

Background: Intradialytic hypotension (IDH) is a critical complication related to worse outcomes in patients undergoing maintenance hemodialysis. Herein, we addressed the impact of IDH on mortality and other outcomes in patients with severe acute kidney injury (AKI) requiring intermittent hemodialysis. Methods: We retrospectively reviewed 1,009 patients who underwent intermittent hemodialysis due to severe AKI. IDH was defined as either dialysis discontinuation due to hemodynamic instability or a decrease in systolic blood pressure (BP) of ≥30 mmHg, with or without a nadir systolic BP of <90 mmHg during the first session. The primary outcome was all-cause mortality, and transfer to the intensive care unit (ICU) due to unstable status was additionally analyzed. Hazard ratios (HRs) of outcomes were calculated using a Cox regression model after adjusting for multiple variables. Risk factors for IDH were evaluated using a logistic regression model. Results: IDH occurred in 449 patients (44.5%) during the first hemodialysis session. Patients with IDH had a higher mortality rate than those without IDH (40% vs. 23%; HR, 1.30; 95% confidence interval [CI], 1.02–1.65). The rate of ICU transfer was higher in patients experiencing IDH than in those without IDH (17% vs. 11%; HR, 1.43; 95% CI, 1.02–2.02). Factors such as old age, high BP and pulse rate, active malignancy, cirrhosis, and hypoalbuminemia were associated with an increased risk of IDH episodes. Conclusion The occurrence of IDH is associated with worse outcomes in patients with AKI requiring intermittent hemodialysis. Therefore, careful monitoring and early intervention of IDH may be necessary in this patient subset.

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 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 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 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.

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development. Methods: At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist. Results: Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3. Conclusion: This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients’ diet/exercise in the future.