Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: This study was a scoping review designed to identify research trends in loneliness and health targeting for domestic and foreign nurses. Methods: The methodological framework was based on a previous work by Arksey and O’Malley. The studies reviewed were found through electronic databases, such as RISS, PubMed, and CINAHL. The period of the data was from January 2000 to December 2021. Results: The 6 studies were reviewed. The loneliness of nurses was found to be experienced when the frequency of social interaction with leaders and colleagues was low, when missing camaraderie and sense of belonging, when work felt meaningless, and when social support level was low. Additionally, loneliness raised levels of depression, anxiety, and stress level while lowered well-being level, and that was shown to have harmful effects for health, such as burnout and fatigue. Also, it was found that loneliness lowered nurses’ job satisfaction and increased their turnover rate. Conclusion Further development of nurses’ loneliness management program is needed. Also, it is suggested that the additional studies to verify causal relationship and mechanism between loneliness and health are required.

Purpose: This study was a scoping review designed to identify research trends in loneliness and health targeting for domestic and foreign nurses. Methods: The methodological framework was based on a previous work by Arksey and O’Malley. The studies reviewed were found through electronic databases, such as RISS, PubMed, and CINAHL. The period of the data was from January 2000 to December 2021. Results: The 6 studies were reviewed. The loneliness of nurses was found to be experienced when the frequency of social interaction with leaders and colleagues was low, when missing camaraderie and sense of belonging, when work felt meaningless, and when social support level was low. Additionally, loneliness raised levels of depression, anxiety, and stress level while lowered well-being level, and that was shown to have harmful effects for health, such as burnout and fatigue. Also, it was found that loneliness lowered nurses’ job satisfaction and increased their turnover rate. Conclusion Further development of nurses’ loneliness management program is needed. Also, it is suggested that the additional studies to verify causal relationship and mechanism between loneliness and health are required.

Purpose: This study was a scoping review designed to identify research trends in loneliness and health targeting for domestic and foreign nurses. Methods: The methodological framework was based on a previous work by Arksey and O’Malley. The studies reviewed were found through electronic databases, such as RISS, PubMed, and CINAHL. The period of the data was from January 2000 to December 2021. Results: The 6 studies were reviewed. The loneliness of nurses was found to be experienced when the frequency of social interaction with leaders and colleagues was low, when missing camaraderie and sense of belonging, when work felt meaningless, and when social support level was low. Additionally, loneliness raised levels of depression, anxiety, and stress level while lowered well-being level, and that was shown to have harmful effects for health, such as burnout and fatigue. Also, it was found that loneliness lowered nurses’ job satisfaction and increased their turnover rate. Conclusion Further development of nurses’ loneliness management program is needed. Also, it is suggested that the additional studies to verify causal relationship and mechanism between loneliness and health are required.

Purpose: Depression and frailty are common health problems that occur separately or simultaneously in later life. The two syndromes are correlated, but they need to be distinguished to promote successful aging. Previous studies have examined the reciprocal relationship between depression and frailty, but there are limitations in the methods or statistical analysis. This study aims to confirm the potential prospective bidirectional and causal relationship between depression and frailty. Methods: We used data from 887 older adults aged 70 to 84 from the Korean Frailty and Aging Cohort Study (KFACS) in 2016, 2018, and 2020 (3 waves). We separated the within-individual process from the stable between-individual differences using the random intercepts cross-lagged panel model. Results: Significant bidirectional causal effects were observed in 2 paths. Older adults with higher depression than their within-person average at T1 had a higher risk of frailty at T2 (β=.22, p=.008). Subsequently, older adults with higher-than-average frailty scores at T2 showed higher depression at T3 (β=.14, p=.010). Autoregressive effects were only significant from T2 to T3 for both constructs (Depression: β=.16, p=.044; Frailty: β=.13, p=.028). At the between-person level, the correlation was significant between the random intercepts between depression and frailty (β=.47, p<.001). Conclusions We find that depressed older adults have an increased risk of frailty, which contributes to the onset of depression and the maintenance of frailty. Therefore, interventions for each condition may prevent the entry and worsening of the other condition, as well as prevent comorbidity.

Purpose: This study aimed to comprehensively review the experiences of postpartum depression in men. Methods: A qualitative meta-synthesis suggested by Noblit and Hare was conducted in this study. Three qualitative studies were chosen and synthesized to describe men's experiences of postpartum depression. Results: Four themes emerged as a result of synthesizing: “Frustration stemming from excessive responsibility as a father”, “Life of mine being tied to child-rearing”, “Suppression of emotions due to gender role stereotypes”, and “Communication interruption with wife due to marital conflict”. Conclusion The results of this study provided a deeper understanding of the experiences of postpartum depression in men, and can help establish prevention program and arrang social support for postpartum depression in men.