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 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.

Purpose: The aim of this study was to identify the factors affecting the post-traumatic stress disorder (PTSD) symptoms of intensive care unit (ICU) nurses. The variables include event experiences, cognitive flexibility, and co-worker support. Methods: A survey was conducted among 153 ICU nurses working in a general hospital or an advanced general hospital. The questionnaire was completed between October and December 2018, and 153 copies were used for the final analysis. Data were analyzed using multiple linear regression to determine the factors associated with PTSD symptoms among ICU nurses. Results: The level of PTSD symptoms of ICU nurses was 1.20 ± 0.82 out of 4. Full PTSD, signified by a total score of 25 or more, was reported by 45.1% of the study's 153 participants. The significant influencing factors of PTSD symptoms among ICU nurses were the “experience of traumatic events,” “trusted alliance,” which is a subarea of “coworker support,” and both “control” and “alternative,” which are subareas of “cognitive flexibility.” The explanatory power (49.8%) was statistically significant. Conclusions These results suggest that a program to enhance the cognitive flexibility and coworker support of ICU nurses needs to be developed to reduce the PTSD symptoms of ICU nurses.

The coronavirus disease 2019 (COVID-19) has emerged as a threat to human health and public safety. People of all ages are susceptible to severe acute respiratory syndrome coronavirus 2 infection. However, the clinical manifestations of this infection differ by age. This study purposes to describe healthcare considerations for special populations, such as children, pregnant and lactating women, and older adults, who may have unique healthcare needs, in the pandemic situation. To realize the research purpose, we conducted a review of the practice guidelines of public documents and qualified studies that were published online/offline during a specific period. The review identified current knowledge on care for newborns, children in schools, pregnant women (from antenatal to postpartum care), and older adults suffering from high-risk conditions. Subsequently, we summarize vaccination guidance for special populations and, finally, discuss the issues currently affecting special populations. Therefore, this current knowledge on care for special populations helps nurses to provide accurate information on vaccinations aimed at preventing COVID-19 and protecting the masses from infection. Currently, the scarcity of information on COVID-19 variants necessitates further research on measures to reduce pandemic spread.