Purpose: This study aimed to develop a no-contact health promotion behavior program for nursing students as representatives of young adults and to evaluate its effects and applicability. Methods: We employed a one-group pretest/posttest design to develop a no-contact health promotion behavior program for young adults and to assess its impacts on post-pandemic health promotion behavior, health self-efficacy, depression, and stress among nursing students. Using an online ad, we recruited young adults aged 19 to 29 living in South Korea who were attending nursing school; those who provided informed consent to participate in the study were enrolled. Results: The no-contact health promotion behavior program was effective at improving health promotion behavior (Z=-2.90, p=.004) and health self-efficacy (Z=-2.24, p=.025) and at alleviating depression (Z=-2.13, p=.033). Conclusion This study confirmed the potential of a no-contact program to advance health management among young adults. It also substantiated the program’s effects on fostering experiences and promoting personal health among nursing students, who are prospective healthcare professionals.

Purpose: This study aimed to develop a no-contact health promotion behavior program for nursing students as representatives of young adults and to evaluate its effects and applicability. Methods: We employed a one-group pretest/posttest design to develop a no-contact health promotion behavior program for young adults and to assess its impacts on post-pandemic health promotion behavior, health self-efficacy, depression, and stress among nursing students. Using an online ad, we recruited young adults aged 19 to 29 living in South Korea who were attending nursing school; those who provided informed consent to participate in the study were enrolled. Results: The no-contact health promotion behavior program was effective at improving health promotion behavior (Z=-2.90, p=.004) and health self-efficacy (Z=-2.24, p=.025) and at alleviating depression (Z=-2.13, p=.033). Conclusion This study confirmed the potential of a no-contact program to advance health management among young adults. It also substantiated the program’s effects on fostering experiences and promoting personal health among nursing students, who are prospective healthcare professionals.

Purpose: This study aimed to develop a no-contact health promotion behavior program for nursing students as representatives of young adults and to evaluate its effects and applicability. Methods: We employed a one-group pretest/posttest design to develop a no-contact health promotion behavior program for young adults and to assess its impacts on post-pandemic health promotion behavior, health self-efficacy, depression, and stress among nursing students. Using an online ad, we recruited young adults aged 19 to 29 living in South Korea who were attending nursing school; those who provided informed consent to participate in the study were enrolled. Results: The no-contact health promotion behavior program was effective at improving health promotion behavior (Z=-2.90, p=.004) and health self-efficacy (Z=-2.24, p=.025) and at alleviating depression (Z=-2.13, p=.033). Conclusion This study confirmed the potential of a no-contact program to advance health management among young adults. It also substantiated the program’s effects on fostering experiences and promoting personal health among nursing students, who are prospective healthcare professionals.

Purpose: This study aimed to identify the factors associated with the progression from suicidal ideation to suicide attempts and to compare the predictive performance of various machine learning models. Methods: We conducted a secondary analysis using original data from the 20th Korea Youth Risk Behavior Survey (KYRBS), focusing on 6,316 adolescents who reported suicidal ideation. We evaluated predictive performance using logistic regression, random forest, and k-nearest neighbors (KNN) models. Results: Suicide attempts were significantly associated with sociodemographic factors, such as academic achievement, economic status, type of residence, and perceived health status, as well as psychological and behavioral factors, including suicidal planning, feelings of sadness and despair, anxiety, alcohol use, smoking, drug use, and exposure to violence. Logistic regression exhibited the highest predictive performance (AUC=0.77, accuracy=0.84, F1=0.80). The random forest model identified suicidal planning, loneliness, generalized anxiety, drug use, and exposure to violence as key predictors based on the Gini index, while KNN demonstrated the lowest predictive stability. Conclusion Logistic regression is effective for predicting suicide attempts among adolescents, and machine learning approaches should be considered for early risk screening in community mental health nursing.

Purpose: This study aimed to develop and compare machine learning models for predicting prediabetes in young adults in Korea using dietary intake data and to identify the most effective model. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey were used, with 823 participants aged 19–35 years selected after excluding those with missing data. Logistic regression, k-nearest neighbors, and random forest models were applied to predict prediabetes, and the analysis was conducted using the Orange 3.5 program. Five-fold cross-validation was performed to reduce performance variability, and test data were used for final model validation. Results: In the dataset, 14%–15% of participants were classified as having prediabetes. The random forest model showed the highest performance in terms of classification accuracy, harmonic mean of precision and recall, and precision. Logistic regression had the highest performance regarding the model’s ability to distinguish between individuals with and without prediabetes. Age, thiamine intake, and water intake emerged as the most important predictors. Conclusion This study demonstrated the utility of using dietary intake data to predict prediabetes in young adults. The random forest model provided the highest prediction accuracy, supporting early detection and intervention, which could help to reduce unnecessary treatment. This highlights nurses’ important role in educating patients about lifestyle changes and implementing preventive care. Future studies should incorporate additional factors, such as psychological and lifestyle variables, to improve the model's performance.

Purpose: This study aimed to develop and compare machine learning models for predicting prediabetes in young adults in Korea using dietary intake data and to identify the most effective model. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey were used, with 823 participants aged 19–35 years selected after excluding those with missing data. Logistic regression, k-nearest neighbors, and random forest models were applied to predict prediabetes, and the analysis was conducted using the Orange 3.5 program. Five-fold cross-validation was performed to reduce performance variability, and test data were used for final model validation. Results: In the dataset, 14%–15% of participants were classified as having prediabetes. The random forest model showed the highest performance in terms of classification accuracy, harmonic mean of precision and recall, and precision. Logistic regression had the highest performance regarding the model’s ability to distinguish between individuals with and without prediabetes. Age, thiamine intake, and water intake emerged as the most important predictors. Conclusion This study demonstrated the utility of using dietary intake data to predict prediabetes in young adults. The random forest model provided the highest prediction accuracy, supporting early detection and intervention, which could help to reduce unnecessary treatment. This highlights nurses’ important role in educating patients about lifestyle changes and implementing preventive care. Future studies should incorporate additional factors, such as psychological and lifestyle variables, to improve the model's performance.

Purpose: This study aimed to develop and compare machine learning models for predicting prediabetes in young adults in Korea using dietary intake data and to identify the most effective model. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey were used, with 823 participants aged 19–35 years selected after excluding those with missing data. Logistic regression, k-nearest neighbors, and random forest models were applied to predict prediabetes, and the analysis was conducted using the Orange 3.5 program. Five-fold cross-validation was performed to reduce performance variability, and test data were used for final model validation. Results: In the dataset, 14%–15% of participants were classified as having prediabetes. The random forest model showed the highest performance in terms of classification accuracy, harmonic mean of precision and recall, and precision. Logistic regression had the highest performance regarding the model’s ability to distinguish between individuals with and without prediabetes. Age, thiamine intake, and water intake emerged as the most important predictors. Conclusion This study demonstrated the utility of using dietary intake data to predict prediabetes in young adults. The random forest model provided the highest prediction accuracy, supporting early detection and intervention, which could help to reduce unnecessary treatment. This highlights nurses’ important role in educating patients about lifestyle changes and implementing preventive care. Future studies should incorporate additional factors, such as psychological and lifestyle variables, to improve the model's performance.

Purpose: This study aimed to investigate the mediating effects of health promotion behavior (HPB) in the relationship between anxiety and quality of life (QoL) in young adults living in the post-pandemic era. Methods: A cross-sectionaldescriptiveonlinesurveydesign was utilized. Data on anxiety, QoL, HPB, and demographic characteristics were collected from 213 adults aged 19–35 years in Korea via an online survey in January 2024. The collected data were analyzed using SPSS 27.0 and PROCESS MACRO 4.2 software. Results: Strong correlations were observed among anxiety, QoL, and post-pandemic HPB (PP-HPB) in young adults, andanxiety and PP-HPB were identified as significant predictors of QoL. The total effect of anxiety on QoL was significant (B = −1.40, bootstrapped SE = 0.10), with both the direct effect (B = −0.70, bootstrapped SE = 0.09) and the indirect effect (B = −0.70, bootstrapped SE = 0.11) being significant. This suggests that PP-HPB partially mediated the relationship between anxiety and QoL. Conclusion This study highlights the importance of strengthening HPB with consideration of life changes since the coronavirus disease 2019 pandemic to improve QoL among young adults with anxiety.

Purpose: This study aimed to develop and compare machine learning models for predicting prediabetes in young adults in Korea using dietary intake data and to identify the most effective model. Methods: Data from the ninth Korea National Health and Nutrition Examination Survey were used, with 823 participants aged 19–35 years selected after excluding those with missing data. Logistic regression, k-nearest neighbors, and random forest models were applied to predict prediabetes, and the analysis was conducted using the Orange 3.5 program. Five-fold cross-validation was performed to reduce performance variability, and test data were used for final model validation. Results: In the dataset, 14%–15% of participants were classified as having prediabetes. The random forest model showed the highest performance in terms of classification accuracy, harmonic mean of precision and recall, and precision. Logistic regression had the highest performance regarding the model’s ability to distinguish between individuals with and without prediabetes. Age, thiamine intake, and water intake emerged as the most important predictors. Conclusion This study demonstrated the utility of using dietary intake data to predict prediabetes in young adults. The random forest model provided the highest prediction accuracy, supporting early detection and intervention, which could help to reduce unnecessary treatment. This highlights nurses’ important role in educating patients about lifestyle changes and implementing preventive care. Future studies should incorporate additional factors, such as psychological and lifestyle variables, to improve the model's performance.

Purpose: A descriptive survey-based study was undertaken to determine how emotional intelligence mediates the relationship between social support and resilience by clinical nurses, thereby providing primary data for improving resilience. Methods: This study involved a descriptive survey of 202 nurses working in four general hospitals. Using SPSS/WIN 26.0, frequency analysis, descriptive statistics, and multiple regression analyses were conducted. Results: Social support had a statistically significant positive correlation with emotional intelligence (β=.49, p<.001) and resilience (β=.47, p<.001). Emotional intelligence showed a statistically significant positive correlation with resilience (β=.66, p<.001). Emotional intelligence was found to have a partial mediation effect on the relationship between social support and resilience (z=5.76, p<.001). Conclusion The study also discovered that social support and emotional intelligence are factors influencing clinical nurses' resilience. Furthermore, it evident that emotional intelligence has a partial mediating effect on the relationship between social support and resilience. Therefore, it is necessary to consider nurses’ emotional intelligence at the individual level to effectively improve resilience through social support.