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 identify core domains for pre-registered nurses by comparing licensing competencies with program outcomes (POs) in undergraduate nursing education. This was accomplished in preparation for the transition of the Korean Nurse Licensing Examination (KNLE) from a tradition seven-subject format to a newly integrated, competency-based single-subject format that reflects current trends in nursing assessment. Methods: A literature review and survey were conducted. From 828 studies retrieved via PubMed, CINAHL, and Google Scholar using keywords such as “newly graduated registered nurses” and “competency OR competence,” 18 were selected according to pre-established inclusion and exclusion criteria. Documents from national and international nursing organizations were included to extract relevant licensing competencies. We also reviewed POs from all undergraduate nursing schools in South Korea to align educational outcomes with the identified core domains. Results: The core domains identified were clinical performance and decision-making, professional attitudes and ethics, communication and interpersonal skills, leadership and teamwork, quality improvement and safety, health promotion and prevention, and information technology and digital health. These domains showed strong alignment with POs under the fourth-cycle accreditation standards. Conclusion It concludes the seven core domains will be appropriate for evaluating pre-registered nurses in the integrated KNLE. Based on the seven identified core domains, expert consensus should be sought in the next phase to support the development of integrated, competency-based test items grounded in these domains.

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 identify core domains for pre-registered nurses by comparing licensing competencies with program outcomes (POs) in undergraduate nursing education. This was accomplished in preparation for the transition of the Korean Nurse Licensing Examination (KNLE) from a tradition seven-subject format to a newly integrated, competency-based single-subject format that reflects current trends in nursing assessment. Methods: A literature review and survey were conducted. From 828 studies retrieved via PubMed, CINAHL, and Google Scholar using keywords such as “newly graduated registered nurses” and “competency OR competence,” 18 were selected according to pre-established inclusion and exclusion criteria. Documents from national and international nursing organizations were included to extract relevant licensing competencies. We also reviewed POs from all undergraduate nursing schools in South Korea to align educational outcomes with the identified core domains. Results: The core domains identified were clinical performance and decision-making, professional attitudes and ethics, communication and interpersonal skills, leadership and teamwork, quality improvement and safety, health promotion and prevention, and information technology and digital health. These domains showed strong alignment with POs under the fourth-cycle accreditation standards. Conclusion It concludes the seven core domains will be appropriate for evaluating pre-registered nurses in the integrated KNLE. Based on the seven identified core domains, expert consensus should be sought in the next phase to support the development of integrated, competency-based test items grounded in these domains.

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 identify core domains for pre-registered nurses by comparing licensing competencies with program outcomes (POs) in undergraduate nursing education. This was accomplished in preparation for the transition of the Korean Nurse Licensing Examination (KNLE) from a tradition seven-subject format to a newly integrated, competency-based single-subject format that reflects current trends in nursing assessment. Methods: A literature review and survey were conducted. From 828 studies retrieved via PubMed, CINAHL, and Google Scholar using keywords such as “newly graduated registered nurses” and “competency OR competence,” 18 were selected according to pre-established inclusion and exclusion criteria. Documents from national and international nursing organizations were included to extract relevant licensing competencies. We also reviewed POs from all undergraduate nursing schools in South Korea to align educational outcomes with the identified core domains. Results: The core domains identified were clinical performance and decision-making, professional attitudes and ethics, communication and interpersonal skills, leadership and teamwork, quality improvement and safety, health promotion and prevention, and information technology and digital health. These domains showed strong alignment with POs under the fourth-cycle accreditation standards. Conclusion It concludes the seven core domains will be appropriate for evaluating pre-registered nurses in the integrated KNLE. Based on the seven identified core domains, expert consensus should be sought in the next phase to support the development of integrated, competency-based test items grounded in these domains.

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 identify core domains for pre-registered nurses by comparing licensing competencies with program outcomes (POs) in undergraduate nursing education. This was accomplished in preparation for the transition of the Korean Nurse Licensing Examination (KNLE) from a tradition seven-subject format to a newly integrated, competency-based single-subject format that reflects current trends in nursing assessment. Methods: A literature review and survey were conducted. From 828 studies retrieved via PubMed, CINAHL, and Google Scholar using keywords such as “newly graduated registered nurses” and “competency OR competence,” 18 were selected according to pre-established inclusion and exclusion criteria. Documents from national and international nursing organizations were included to extract relevant licensing competencies. We also reviewed POs from all undergraduate nursing schools in South Korea to align educational outcomes with the identified core domains. Results: The core domains identified were clinical performance and decision-making, professional attitudes and ethics, communication and interpersonal skills, leadership and teamwork, quality improvement and safety, health promotion and prevention, and information technology and digital health. These domains showed strong alignment with POs under the fourth-cycle accreditation standards. Conclusion It concludes the seven core domains will be appropriate for evaluating pre-registered nurses in the integrated KNLE. Based on the seven identified core domains, expert consensus should be sought in the next phase to support the development of integrated, competency-based test items grounded in these domains.

Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Objectives: Eveningness predominates in adolescence. Morningness has been reported to be associated with mental toughness via resilience. Morningness could be related to healthy sleep habits and a good personality. Methods: Sleep-related behaviors, such as weekend oversleep, social jetlag, and daytime sunlight exposure between 10:00 and 15:00, were evaluated using sleep-related questionnaires. Morningness-eveningness questionnaire, insomnia severity index, Epworth sleepiness scale, hospital anxiety and depression scale and defense style questionnaire were used. This study includes 1,610 high school students. Results: Multiple linear regression analysis revealed that morningness is predicted by male sex (β=0.115, t=4.769, p<0.001) and greater adaptive (β=0.092, t=3.506, p<0.001) and self-suppressive defense styles (β=0.072, t=2.192, p=0.029) and lesser immature defense styles (β=-0.163, t=-4.782, p<0.001) after controlling for age, sleep quality, depression, anxiety. Morningness is also predicted by greater sublimation (β=0.09, t=3.541, p<0.001) and reaction formation (β=0.112, t=4.488, p<0.001) and lesser consumption (β=-0.062, t=-2.501, p=0.012), passive aggression (β=-0.101, t=-3.747, p<0.001) and withdrawal (β=-0.084, t=-2.955, p=0.003), and male sex (β=0.117, t=4.896, p<0.001) after controlling for age, sleep quality, depression, and anxiety. Conclusion Morniness in adolescence may be related to a positive personality. Promoting morningness might be related to healthy development in personality.