Purpose: The purpose of this systematic review and meta-analysis was to investigate the effects of advance care planning on end-of-life decision-making. Methods: Databases including RISS, KISS, KMbase, KoreaMed, PubMed (MEDLINE), Embase, and CINAHL were searched for studies that examined the effects of advance care planning interventions. The inclusion criteria were original studies in English or Korean; adults ≥18 years of age (population); advance care planning (intervention); completion of advance directives (AD) or advance care planning (ACP) (outcomes); and randomized or non-randomized controlled trials (RCTs and non-RCTs, respectively) (design). Study quality was measured using the checklists of the Joanna Briggs Institute. Meta-analyses were conducted with the Comprehensive Meta-Analysis program. Results: Nine RCTs and nine non-RCTs were selected for the final analysis. The effect sizes (ES) of the outcome variables in nine RCTs were meta-analyzed, and found to range from 0.142 to 0.496 for the completion of AD and ACP (ES=0.496, 95% CI: 0.157~0.836), discussion of end-of-life care (ES=0.429, 95% CI: -0.027~0.885), quality of communication (ES=0.413, 95% CI: 0.008~0.818), decisional conflict (ES=0.349, 95% CI: -0.059~0.758), and congruence between preferences for care and delivered care (ES=0.142, 95% CI: -0.267~0.552). Conclusion ACP interventions had a positive effect on the completion of AD and ACP. To apply AD or ACP in Korea, it is necessary to develop ACP interventions that reflect aspects of Korean culture.

Purpose: This study aimed to investigate advance care planning needs expressed online. Methods: This study collected data from online community posts and healthcare news sites.The search keywords included “death,” “euthanasia,” “life-sustaining medical care,” “lifesustaining treatment,” “advance directives,” “advance medical directives,” and “advance care planning.” Data collection spanned from February 2018 to February 14, 2020. Out of 2,288 posts, 1,190 were included in the final analysis. Data analysis was conducted using NVivo 12, a qualitative data analysis software program. Results: Content analysis categorized patients' advance care planning needs into eight themes, 11 theme clusters, and 33 meaningful statements. Similarly, care providers' advance care planning needs were categorized into eight themes, 14 theme clusters, and 42 meaningful statements. The identified themes of care needs included life-sustaining medical care, decision-making related to life-sustaining medical care, physical care, environmental care, supportive and spiritual care, respect, preparing for death, and family. Conclusion This study identified care needs from the perspectives of patients and their families. The findings may serve as preliminary data for future research and clinical applications.

Purpose: This study aimed to investigate advance care planning needs expressed online. Methods: This study collected data from online community posts and healthcare news sites.The search keywords included “death,” “euthanasia,” “life-sustaining medical care,” “lifesustaining treatment,” “advance directives,” “advance medical directives,” and “advance care planning.” Data collection spanned from February 2018 to February 14, 2020. Out of 2,288 posts, 1,190 were included in the final analysis. Data analysis was conducted using NVivo 12, a qualitative data analysis software program. Results: Content analysis categorized patients' advance care planning needs into eight themes, 11 theme clusters, and 33 meaningful statements. Similarly, care providers' advance care planning needs were categorized into eight themes, 14 theme clusters, and 42 meaningful statements. The identified themes of care needs included life-sustaining medical care, decision-making related to life-sustaining medical care, physical care, environmental care, supportive and spiritual care, respect, preparing for death, and family. Conclusion This study identified care needs from the perspectives of patients and their families. The findings may serve as preliminary data for future research and clinical applications.

Purpose: This study aimed to investigate advance care planning needs expressed online. Methods: This study collected data from online community posts and healthcare news sites.The search keywords included “death,” “euthanasia,” “life-sustaining medical care,” “lifesustaining treatment,” “advance directives,” “advance medical directives,” and “advance care planning.” Data collection spanned from February 2018 to February 14, 2020. Out of 2,288 posts, 1,190 were included in the final analysis. Data analysis was conducted using NVivo 12, a qualitative data analysis software program. Results: Content analysis categorized patients' advance care planning needs into eight themes, 11 theme clusters, and 33 meaningful statements. Similarly, care providers' advance care planning needs were categorized into eight themes, 14 theme clusters, and 42 meaningful statements. The identified themes of care needs included life-sustaining medical care, decision-making related to life-sustaining medical care, physical care, environmental care, supportive and spiritual care, respect, preparing for death, and family. Conclusion This study identified care needs from the perspectives of patients and their families. The findings may serve as preliminary data for future research and clinical applications.

Purpose: This study aimed to investigate advance care planning needs expressed online. Methods: This study collected data from online community posts and healthcare news sites.The search keywords included “death,” “euthanasia,” “life-sustaining medical care,” “lifesustaining treatment,” “advance directives,” “advance medical directives,” and “advance care planning.” Data collection spanned from February 2018 to February 14, 2020. Out of 2,288 posts, 1,190 were included in the final analysis. Data analysis was conducted using NVivo 12, a qualitative data analysis software program. Results: Content analysis categorized patients' advance care planning needs into eight themes, 11 theme clusters, and 33 meaningful statements. Similarly, care providers' advance care planning needs were categorized into eight themes, 14 theme clusters, and 42 meaningful statements. The identified themes of care needs included life-sustaining medical care, decision-making related to life-sustaining medical care, physical care, environmental care, supportive and spiritual care, respect, preparing for death, and family. Conclusion This study identified care needs from the perspectives of patients and their families. The findings may serve as preliminary data for future research and clinical applications.

The purposes of this study were to determine the effects of a disaster training education for hospital nurses in terms of attitudes, preparedness, and competence of disaster nursing. Methods: One group pretest and posttest design was adopted. Data were collected from April 20th to May 20th, 2017 using 80 hospital nurses enrolled at a tertiary hospital located in D city. Results: The levels of attitudes (t=-2.31, p=.024), preparedness (t=-7.67, p<.001), and competencies (t=-16.82, p<.001) of disaster nursing at the posttest were significantly higher than those of the pretest. There were statistically significant relationships among nurses' attitudes, preparedness, and competencies of disaster nursing. The attitudes and preparedness of disaster nursing had significant effects on the competencies of disaster nursing. Conclusion: Disaster training education improved attitudes, preparedness, and competencies of disaster nursing in hospital nurses. Therefore, it is recommended to develop more practical disaster training education programs toward hospital nurses as well as nursing students as a way to enhance the competence of disaster nursing.

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

The peer review process ensures the integrity of scientific research. This is particularly important in the medical field, where research findings directly impact patient care. However, the rapid growth of publications has strained reviewers, causing delays and potential declines in quality. Generative artificial intelligence, especially large language models (LLMs) such as ChatGPT, may assist researchers with efficient, high-quality reviews. This review explores the integration of LLMs into peer review, highlighting their strengths in linguistic tasks and challenges in assessing scientific validity, particularly in clinical medicine. Key points for integration include initial screening, reviewer matching, feedback support, and language review. However, implementing LLMs for these purposes will necessitate addressing biases, privacy concerns, and data confidentiality. We recommend using LLMs as complementary tools under clear guidelines to support, not replace, human expertise in maintaining rigorous peer review standards.

PURPOSE: The purpose of this study was to demonstrate the effects of visiting nursing services in long-term care (LTC) insurance on changes in health status and physical functions among the elderly. METHODS: We analyzed survey data on the living status and welfare needs of the elderly from 2008 and 2011 using the difference-in-difference method with propensity score matching. RESULTS: The subjects were 76 elders including 38 visiting nursing service users(intervention group) and 38 home-based LTC service users (control group). Results from DID analysis confirmed that the subjective health status level of the intervention group was 2.2 points higher (p=.044) and the depression level was 0.3 point lower (p=.039) than the control group. In addition, the intervention group's ADL score was 1.9 (p=.027) and IADL 3.9 (p=.030), showing that their health was deteriorated less. CONCLUSION: Visiting nursing service in LTC insurance was associated with delayed deterioration of subjective health status, depression, ADL and IADL. These findings suggest the need of rebuilding visiting nursing service programs focusing on prevention services, which will be more contributive to elderly health care and the reduction of social costs.
Activities of Daily Living ; Aged ; Delivery of Health Care ; Depression ; Humans ; Insurance ; Insurance, Long-Term Care* ; Long-Term Care ; Nursing Services* ; Propensity Score