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.

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.

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 investigate the differences of results of Older Adult Behavior Checklist (OABCL) in subjects with dementia, mild cognitive impairment (MCI), and normal group. METHODS: The data was composed of 42 patients with MCI, 71 patients with dementia, and 111 randomly collected participants who were recruited for standardization of Korean version of Achenbach System of Empirically Based Assessment Older Adult Forms. Medical records, results of OABCL, neuropsychological tests, activities of daily living scale, and clinical dementia rating scale of the subjects were retrospectively analyzed to find significant factors in distinguishing the groups. RESULTS: In dementia group, almost of the empirically base problem scales and Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scales showed significantly higher scores than MCI or normal groups. MCI group also showed higher scores in several empirically base problem and DSM-oriented scales than normal group. Also, functional impairment, memory/cognition, thought problems, irritable/disinhibited scales of empirically base problem and depressive, dementia, psychotic problems DSM-oriented scales significantly predict in distinguishing the three groups. CONCLUSION: The results implicated that OABCL is not only useful in assessing cognition decline but also in investigating psychological and behavioral problems of older adults.
Activities of Daily Living ; Adult* ; Checklist* ; Cognition ; Cognition Disorders* ; Dementia ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Mass Screening* ; Medical Records ; Mild Cognitive Impairment ; Neuropsychological Tests ; Problem Behavior ; Psychopathology* ; Retrospective Studies ; Weights and Measures

Objective: This study aimed to investigate how shift-working nursing professionals perceive the current lights-off time in wards as early, appropriate, or late and how their perceptions can be influenced when considering people’s usual bedtimes. Methods: An online survey was conducted comprising queries about the current lights-off time in wards and respondents’ opinions, self-rated psychological status, and perceptions of the current lights-off time considering others’ usual bedtimes. Psychological status was evaluated using the Insomnia Severity Index, the Patient Health Questionnaire-9, the Dysfunctional Beliefs and Attitudes about Sleep-16, and the Discrepancy between Desired Time in Bed and Desired Total Sleep Time (DBST) Index, along with the expected DBST Index of others. Results: Of 159 nursing professionals, 88.7% regarded the current lights-off time of 9:46±0:29 PM as appropriate. However, when considering others’ usual bedtimes, the proportion perceiving the lights-off time as too early rose from 6.9% to 28.3%. Participants recommended delaying the lights-off time to 10:06±0:42 PM for patients’ sleep and 10.22±0:46 PM for nursing care activities. Nursing professionals’ insomnia severity was significantly higher among who responded that current light off time is too early after considering usual bedtime of other people. Conclusion This study underscores the need to reassess lights-off times in wards given individuals’ typical bedtimes. The findings emphasize the need to address nursing professionals’ perspectives and insomnia severity when optimizing lights-off schedules in healthcare settings.

Objective: This study aimed to investigate how shift-working nursing professionals perceive the current lights-off time in wards as early, appropriate, or late and how their perceptions can be influenced when considering people’s usual bedtimes. Methods: An online survey was conducted comprising queries about the current lights-off time in wards and respondents’ opinions, self-rated psychological status, and perceptions of the current lights-off time considering others’ usual bedtimes. Psychological status was evaluated using the Insomnia Severity Index, the Patient Health Questionnaire-9, the Dysfunctional Beliefs and Attitudes about Sleep-16, and the Discrepancy between Desired Time in Bed and Desired Total Sleep Time (DBST) Index, along with the expected DBST Index of others. Results: Of 159 nursing professionals, 88.7% regarded the current lights-off time of 9:46±0:29 PM as appropriate. However, when considering others’ usual bedtimes, the proportion perceiving the lights-off time as too early rose from 6.9% to 28.3%. Participants recommended delaying the lights-off time to 10:06±0:42 PM for patients’ sleep and 10.22±0:46 PM for nursing care activities. Nursing professionals’ insomnia severity was significantly higher among who responded that current light off time is too early after considering usual bedtime of other people. Conclusion This study underscores the need to reassess lights-off times in wards given individuals’ typical bedtimes. The findings emphasize the need to address nursing professionals’ perspectives and insomnia severity when optimizing lights-off schedules in healthcare settings.

Objective: This study aimed to investigate how shift-working nursing professionals perceive the current lights-off time in wards as early, appropriate, or late and how their perceptions can be influenced when considering people’s usual bedtimes. Methods: An online survey was conducted comprising queries about the current lights-off time in wards and respondents’ opinions, self-rated psychological status, and perceptions of the current lights-off time considering others’ usual bedtimes. Psychological status was evaluated using the Insomnia Severity Index, the Patient Health Questionnaire-9, the Dysfunctional Beliefs and Attitudes about Sleep-16, and the Discrepancy between Desired Time in Bed and Desired Total Sleep Time (DBST) Index, along with the expected DBST Index of others. Results: Of 159 nursing professionals, 88.7% regarded the current lights-off time of 9:46±0:29 PM as appropriate. However, when considering others’ usual bedtimes, the proportion perceiving the lights-off time as too early rose from 6.9% to 28.3%. Participants recommended delaying the lights-off time to 10:06±0:42 PM for patients’ sleep and 10.22±0:46 PM for nursing care activities. Nursing professionals’ insomnia severity was significantly higher among who responded that current light off time is too early after considering usual bedtime of other people. Conclusion This study underscores the need to reassess lights-off times in wards given individuals’ typical bedtimes. The findings emphasize the need to address nursing professionals’ perspectives and insomnia severity when optimizing lights-off schedules in healthcare settings.

Objective: This study aimed to investigate how shift-working nursing professionals perceive the current lights-off time in wards as early, appropriate, or late and how their perceptions can be influenced when considering people’s usual bedtimes. Methods: An online survey was conducted comprising queries about the current lights-off time in wards and respondents’ opinions, self-rated psychological status, and perceptions of the current lights-off time considering others’ usual bedtimes. Psychological status was evaluated using the Insomnia Severity Index, the Patient Health Questionnaire-9, the Dysfunctional Beliefs and Attitudes about Sleep-16, and the Discrepancy between Desired Time in Bed and Desired Total Sleep Time (DBST) Index, along with the expected DBST Index of others. Results: Of 159 nursing professionals, 88.7% regarded the current lights-off time of 9:46±0:29 PM as appropriate. However, when considering others’ usual bedtimes, the proportion perceiving the lights-off time as too early rose from 6.9% to 28.3%. Participants recommended delaying the lights-off time to 10:06±0:42 PM for patients’ sleep and 10.22±0:46 PM for nursing care activities. Nursing professionals’ insomnia severity was significantly higher among who responded that current light off time is too early after considering usual bedtime of other people. Conclusion This study underscores the need to reassess lights-off times in wards given individuals’ typical bedtimes. The findings emphasize the need to address nursing professionals’ perspectives and insomnia severity when optimizing lights-off schedules in healthcare settings.

Objective: This study aimed to investigate how shift-working nursing professionals perceive the current lights-off time in wards as early, appropriate, or late and how their perceptions can be influenced when considering people’s usual bedtimes. Methods: An online survey was conducted comprising queries about the current lights-off time in wards and respondents’ opinions, self-rated psychological status, and perceptions of the current lights-off time considering others’ usual bedtimes. Psychological status was evaluated using the Insomnia Severity Index, the Patient Health Questionnaire-9, the Dysfunctional Beliefs and Attitudes about Sleep-16, and the Discrepancy between Desired Time in Bed and Desired Total Sleep Time (DBST) Index, along with the expected DBST Index of others. Results: Of 159 nursing professionals, 88.7% regarded the current lights-off time of 9:46±0:29 PM as appropriate. However, when considering others’ usual bedtimes, the proportion perceiving the lights-off time as too early rose from 6.9% to 28.3%. Participants recommended delaying the lights-off time to 10:06±0:42 PM for patients’ sleep and 10.22±0:46 PM for nursing care activities. Nursing professionals’ insomnia severity was significantly higher among who responded that current light off time is too early after considering usual bedtime of other people. Conclusion This study underscores the need to reassess lights-off times in wards given individuals’ typical bedtimes. The findings emphasize the need to address nursing professionals’ perspectives and insomnia severity when optimizing lights-off schedules in healthcare settings.

The integrity of the high cervical spine, the transition zone from the brainstem to the spinal cord, is crucial for survival and daily life. The region protects the enclosed neurovascular structure and allows a substantial portion of the head motion. Injuries of the high cervical spine are frequent, and the fractures of the C2 vertebra account for approximately 17–25% of acute cervical fractures. We review the two major types of C2 vertebral fractures, odontoid fracture and Hangman’s fracture. For both types of fractures, favorable outcomes could be obtained if the delicately selected conservative treatment is performed. In odontoid fractures, as the most common fracture on the C2 vertebrae, anterior screw fixation is considered first for type II fractures, and C1–2 fusion is suggested when nonunion is a concern or occurs. Hangman's fractures are the second most common fracture. Many stable extension type I and II fractures can be treated with external immobilization, whereas the predominant flexion type IIA and III fractures require surgical stabilization. No result proves that either anterior or posterior surgery is superior, and the surgeon should decide on the surgical method after careful consideration according to each clinical situation. This review will briefly describe the basic principles and current treatment concepts of C2 fractures.