Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models. Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period 2014–2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics. Results: RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents’ worries, with depression and stress frequently co-occurring. Conclusions This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models. Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period 2014–2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics. Results: RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents’ worries, with depression and stress frequently co-occurring. Conclusions This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models. Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period 2014–2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics. Results: RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents’ worries, with depression and stress frequently co-occurring. Conclusions This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objective: The aim of this study was to explore the psychometric properties of the Insomnia Severity Index (ISI) based on modern test theory, such as item response theory (IRT) and Rasch analysis, with shortened versions of the ISI among the general population. Methods: We conducted two studies to evaluate the reliability and validity of the shortened versions of the ISI in a Korean population. In Study I, conducted via online survey, we performed an exploratory factor analysis (n=400). In Study II, confirmatory factor analysis (CFA) was conducted (n=400). IRT and Rasch analysis were performed on all samples. Participants symptoms were rated using the ISI, Dysfunctional Beliefs and Attitudes about Sleep–16 items, Dysfunctional Beliefs about Sleep–2 items, Patient Health Questionnaire–9 items, and discrepancy between desired time in bed and desired total sleep time. Results: CFA showed a good fit for the 2-factor model of the ISI (comparative fit index=0.994, Tucker–Lewis index=0.990, root-meansquare-error of approximation=0.039, and standardized root-mean-square residual=0.046). The 3-item versions also showed a good fit for the model. All scales showed good internal consistency reliability. The scale information curve of the 2-item scale was similar to that of the full-scale ISI. The Rasch analysis outputs suggested a good model fit. Conclusion The shortened 2-factor ISI is a reliable and valid model for assessing the severity of insomnia in the Korean population. The results are needed to be explored further among the clinical sample of insomnia.

Ataxia is presented by various etiologies, including acquired, genetic and degenerative disorders. Although hereditary ataxia is suspected when typical symptom of ataxia with concurrent is identified, it is sometimes difficult to diagnose hereditary ataxia without genetic test. Clinically, next generation sequencing technology has been developed and widely used for diagnosis of hereditary disease. Hereby, we experienced cases of genetically confirmed OPA1 mutation, which are presented with various clinical manifestations including ataxic gait and decreased visual acuity.