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Purpose: This study aimed to investigate the trends in domestic research related to health literacy in children and adolescents. Methods: Six journal articles and four dissertations or theses related to health literacy in children and adolescents were analyzed according to the following criteria: study aim, research design, study population and sample size, sampling method, health literacy variables, and characteristics of health literacy measurement instruments. Result: The majority of studies focused on general children and adolescents. Correlational studies were most prevalent, while no experimental study was identified at all. The variable most frequently explored in relation to health literacy was health promoting behaviors. Functional health literacy instruments, assessing numeracy and reading comprehension, were most commonly used to evaluate child and adolescent health literacy. Conclusion: Considering the emphasis on health literacy improvement in order to achieve health equity across the entire life cycle, there is a need for both quantitative and qualitative growth in domestic health literacy studies for children and adolescents. School-based interventions are essential for enhancing health literacy. Furthermore, it is also necessary to develop an instrument to measure health literacy of children and adolescents that can be utilized and monitored at the national level.

Purpose: This study aims to identify gender-based differences in factors related to smartphone overdependence among middle school students. Methods: The subjects of the study were middle school students who participated in the 16th Korea Youth Risk Behavior Survey (2020). Frequency analysis, the Rao-Scott x2 test, and multiple logistic regression were performed using SPSS 27.0. Results: Identified risk factors were grade level, self-reported health, self-reported happiness, loneliness, smoking, drinking, poor sleep quality, physical activity, and generalized anxiety disorder, which were commonly found in all participants. Additionally, stress was found related only in boys, while the residential area and depression showed associations only in girls. In particular, the more severe was the level of generalized anxiety disorder, the higher was the risk ratio of the high-risk group, compared to the potential-risk group. Conclusion: Based on the findings of the study, a customized strategy that considers gender differences should be developed in order to prevent smartphone overdependence in middle school students.

Purpose: The purpose of this study was to investigate the mediating effect of college adjustment on the relationship between self-leadership and career decision-making self-efficacy in first-year nursing students. Methods: This descriptive research involved 250 nursing freshmen at two local universities and analyzed data from the final 170 students. Descriptive statistics, the independent t-test, the Kruskal-Wallis test, the Mann-Whitney U test, the Pearson correlation coefficient and multiple regression were employed for data analysis. The mediating effect was assessed following Baron and Kenny’s three-step verification procedure, and the statistical significance of the mediating effect was determined by bootstrapping. Results: Academic adjustment (β=.24, p<.001) and institutional adjustment (β =.20, p=.004), sub-factors of college adjustment, were identified to have a partial mediating effect on the relationship between the subject's self-leadership and career decision-making self-efficacy (β=.47, p<.001). Conclusion: Enhancing the career decision-making self-efficacy of first-year nursing students could become more effective through the development of programs focused on career exploration and counseling, which include content to promote self-leadership and college adjustment (academic adjustment and institutional adjustment).

Purpose: The COVID-19 pandemic, which emerged in late 2019, had a profound impact on global public health and disrupted the daily lives of people worldwide. Particularly, university students faced a challenging situation as their university life underwent a drastic transformation due to long-term remote learning and isolation measures. This study aimed to investigate the relationship between changes in daily life during the 2020 COVID-19 pandemic and depressive symptoms among university students aged between 19 and 29 in Korea. Methods: We analyzed data from the nationally representative 2020 Community Health Survey (CHS). Among the 229,269 participants, 9,279 university students aged 19-29, either enrolled or on leave, were selected. After excluding 401 cases with missing values, the final sample comprised 8,878 individuals. Using multivariate logistic regression with a complex sample design, we explored the association between daily life changes during the COVID-19 pandemic and depressive symptoms. Results: Changes in daily life during the COVID-19 pandemic was associated with depressive symptoms in Korean university students aged 19 to 29, even after adjusting for sociodemographic characteristics, health-related factors, and COVID-19-related aspects (OR=1.28, 95% CI=1.09~1.50). Conclusion: Our study suggests that when examining the impact of COVID-19 on health issues, it is crucial to consider the changes in daily life caused by the pandemic. These findings can provide insights into the psychological well-being of university students during times of crisis.

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were ‘difficulty recovering from daytime fatigue’ and ‘subjective health perception’. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.