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 aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

Purpose: This study aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

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 aimed to investigate the influence of perceptions of patient safety culture, job stress, and nursing work environment on patient safety nursing activities among emergency room nurses. Methods: This correlational study was conducted from June 5 to July 31, 2024, and targeted 114 emergency room nurses in Busan. A structured self-report questionnaire was used to collect data. Descriptive statistics, independent sample t-tests, one-way ANOVA, Pearson's correlation coefficients, and multiple regression analyses were employed for data analysis. Results: Patient safety nursing activities significantly differed by age (F=6.17, p=.001) and total clinical experience (F=8.89, p<.001) among the participants' general characteristics. Positive correlations were identified with perceptions of patient safety culture (r=.70, p<.001) and nursing work environment (r=.27, p=.003). Multiple regression analysis indicated that perception of patient safety culture (β=.72, p<.001) and total clinical experience (β=-.32, p=.011) were significant predictors, accounting for 50.5% (F=20.24, p<.001) of the variance. Conclusion The findings indicated that perceptions of patient safety culture and total clinical experience are critical factors to be considered when designing interventions to enhance patient safety nursing activities among emergency room nurses.

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: The purpose of this study was to examine the effects of a mobile application on quality of life, wellness, and preventive behaviors against dementia among older adults who use senior citizen centers. Methods: Seventy-two older adults who used senior citizen centers were allocated to an intervention group (n=36) and a control group (n=36). The experimental treatment involved a cognitive enhancement program for preventing dementia using a mobile application for 12 sessions over 6 weeks. Data were analyzed using the t-test, chi-square test, Fisher’s exact test, paired t-test, and independent t-test. Results: The cognitive enhancement program based on a mobile application was effective for wellness (t = -3.87, p < .001) and preventive behaviors against dementia (t = -3.98, p < .001) for older adults who used a senior center. Conclusion The mobile application-based cognitive enhancement program developed in this study is recommended as an effective intervention for dementia prevention in older adults.

Objectives: This study aimed to assess hand grip strength and femoral neck bone mineral density levels among Korean fishery workers and investigate their association. Methods: Hand grip strength and femoral neck bone mineral density were measured in a survey and health examination conducted in 2021 among fishery workers in a southern region of South Korea. Covariates including gender, age, education level, income level, smoking behavior, drinking behavior, family history of hip fractures, use of calcium and vitamin D supplements, hypertension, diabetes, regular exercise, and body mass index were investigated. Multiple regression analysis was employed to assess the association between hand grip strength and femoral neck bone mineral density. Results: Among 147 fishery workers, 8.16% exhibited low hand grip strength levels indicative of possible sarcopenia, and a significant association was found between low hand grip strength and decreased femoral neck bone mineral density (β = -89.14, 95% CI = -160.50, -17.78). Additionally, factors such as women gender, advanced age, family history of hip fractures, and a body mass index below 25 kg/m 2 were associated with decreased femoral neck bone mineral density. In the subgroup analysis by gender, a correlation between low hand grip strength and decreased femoral neck bone mineral density was observed only in men. Conclusions Further research is needed to explore various determinants and intervention strategies to prevent musculoskeletal disorders among fishery workers, ultimately enhancing their quality of life and well-being.