PURPOSE: The purpose of this study was to investigate the influencing factors on externalized and internalized problem behaviors among high school students. METHODS: The subjects for this study were 707 students in two high schools in K province. The data were collected during the period from October to November, 2014 by use of questionnaires. The instruments used were the Korean Youth Self-report, Daily Hassles Questionnaire, State-Trait Anger Expression Inventory, and Ego Resiliency Scale. The data were analyzed using SPSS. RESULTS: Significant predictors to explain externalized problem behaviors comprised anger-out, anger-in, anger-control, relation with parents, daily stress, and religion. It was found that these factors explained 46% of externalized problem behavior. Ego resiliency, anger-in, daily stress, gender, relation with parent, and anger-out were significant predictors to explain internalized problem behaviors. It was found that these factors explained 45% of internalized problem behaviors. CONCLUSION: This study suggests that the influencing factors on problem behaviors differ from externalized and internalized problem behaviors. So these findings will provide the basic data to develop a program that is differentiated by problem behavior type.
Adolescent* ; Anger ; Ego ; Humans ; Parents ; Problem Behavior* ; Stress, Psychological

PURPOSE: This study was conducted to examine the mediating effect of meaning in life on the relationship between social connectedness and depression among middle-aged women.METHODS: A descriptive correlational design was employed. One hundred and forty-two middle-aged women who visited welfare centers or churches in Seoul and Gyeonggi Province participated in the study. The data were collected from May to June, 2019 and analyzed using descriptive statistics, Pearson's correlation coefficients, and multiple linear regression analyses based on Baron and Kenny criteria.RESULTS: Social connectedness was significantly positively correlated with meaning in life (r=.52, p < .001) and negatively with depression (r=−.53, p < .001). Meaning in life was also significantly negatively correlated with depression (r=−.50, p < .001). Furthermore, meaning in life had a significant mediating effect on the relationship between social connectedness and depression (β=−.31, p < .001).CONCLUSION: Our study findings suggest that meaning in life plays an important role in maintaining mental health and well-being for middle-aged women. Therefore, it is necessary to develop a nursing intervention program that can enhance the meaning in life to promote mental health and well-being.
Depression ; Female ; Gyeonggi-do ; Humans ; Linear Models ; Mental Health ; Negotiating ; Nursing ; Seoul

No abstract available.
Empyema, Subdural* ; Haemophilus influenzae* ; Haemophilus* ; Meningitis*

Purpose: To assess the effectiveness of an automated screening program that diagnoses horizontal strabismus using machine learning based on ocular deviation data captured by the wearable eyetracker, Tobii pro glasses 2 (TPG2). Methods: The TPG2 which locates the pupil center to measure ocular movement was used. In normal adults wearing TPG2, horizontal ocular deviation was induced by covering the left eye and applying prisms of varying strengths (2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, and 40 PD base-in and out) to the right eye. TPG2 automatically recorded ocular deviation before and after prism induction generating 28 types of ocular deviation sets. From each set, 20 X-axis values before and after ocular deviation were randomly extracted using an oversampling technique creating a total of 61,600 ocular deviation sets. For training, 56,000 sets were used and 5,600 were evaluated for sensitivity, specificity, and area under the curve (AUC). Results: Eleven normal adults (5 males) participated with a mean age of 34.8 ± 7.37 years. Based on an 8 PD threshold, deviations of 8 PD or less demonstrated a sensitivity of 1.0, a specificity of 0.95, and an AUC of 0.97. When categorized into three groups based on 8 PD and 20 PD thresholds, the results were: sensitivity of 0.90 and specificity of 0.95 for ≤ 8 PD; sensitivity of 0.60 and specificity of 1.00 for 8-20 PD; sensitivity of 1.00 and specificity of 0.88 for > 20 PD. Conclusions The machine learning program developed using induced ocular deviations measured with prisms and TPG2 shows promise for use in future strabismus screening tests.

Purpose: To assess the effectiveness of an automated screening program that diagnoses horizontal strabismus using machine learning based on ocular deviation data captured by the wearable eyetracker, Tobii pro glasses 2 (TPG2). Methods: The TPG2 which locates the pupil center to measure ocular movement was used. In normal adults wearing TPG2, horizontal ocular deviation was induced by covering the left eye and applying prisms of varying strengths (2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, and 40 PD base-in and out) to the right eye. TPG2 automatically recorded ocular deviation before and after prism induction generating 28 types of ocular deviation sets. From each set, 20 X-axis values before and after ocular deviation were randomly extracted using an oversampling technique creating a total of 61,600 ocular deviation sets. For training, 56,000 sets were used and 5,600 were evaluated for sensitivity, specificity, and area under the curve (AUC). Results: Eleven normal adults (5 males) participated with a mean age of 34.8 ± 7.37 years. Based on an 8 PD threshold, deviations of 8 PD or less demonstrated a sensitivity of 1.0, a specificity of 0.95, and an AUC of 0.97. When categorized into three groups based on 8 PD and 20 PD thresholds, the results were: sensitivity of 0.90 and specificity of 0.95 for ≤ 8 PD; sensitivity of 0.60 and specificity of 1.00 for 8-20 PD; sensitivity of 1.00 and specificity of 0.88 for > 20 PD. Conclusions The machine learning program developed using induced ocular deviations measured with prisms and TPG2 shows promise for use in future strabismus screening tests.

Purpose: To assess the effectiveness of an automated screening program that diagnoses horizontal strabismus using machine learning based on ocular deviation data captured by the wearable eyetracker, Tobii pro glasses 2 (TPG2). Methods: The TPG2 which locates the pupil center to measure ocular movement was used. In normal adults wearing TPG2, horizontal ocular deviation was induced by covering the left eye and applying prisms of varying strengths (2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, and 40 PD base-in and out) to the right eye. TPG2 automatically recorded ocular deviation before and after prism induction generating 28 types of ocular deviation sets. From each set, 20 X-axis values before and after ocular deviation were randomly extracted using an oversampling technique creating a total of 61,600 ocular deviation sets. For training, 56,000 sets were used and 5,600 were evaluated for sensitivity, specificity, and area under the curve (AUC). Results: Eleven normal adults (5 males) participated with a mean age of 34.8 ± 7.37 years. Based on an 8 PD threshold, deviations of 8 PD or less demonstrated a sensitivity of 1.0, a specificity of 0.95, and an AUC of 0.97. When categorized into three groups based on 8 PD and 20 PD thresholds, the results were: sensitivity of 0.90 and specificity of 0.95 for ≤ 8 PD; sensitivity of 0.60 and specificity of 1.00 for 8-20 PD; sensitivity of 1.00 and specificity of 0.88 for > 20 PD. Conclusions The machine learning program developed using induced ocular deviations measured with prisms and TPG2 shows promise for use in future strabismus screening tests.

Purpose: To assess the effectiveness of an automated screening program that diagnoses horizontal strabismus using machine learning based on ocular deviation data captured by the wearable eyetracker, Tobii pro glasses 2 (TPG2). Methods: The TPG2 which locates the pupil center to measure ocular movement was used. In normal adults wearing TPG2, horizontal ocular deviation was induced by covering the left eye and applying prisms of varying strengths (2, 3, 4, 5, 6, 8, 10, 12, 15, 20, 25, 30, 35, and 40 PD base-in and out) to the right eye. TPG2 automatically recorded ocular deviation before and after prism induction generating 28 types of ocular deviation sets. From each set, 20 X-axis values before and after ocular deviation were randomly extracted using an oversampling technique creating a total of 61,600 ocular deviation sets. For training, 56,000 sets were used and 5,600 were evaluated for sensitivity, specificity, and area under the curve (AUC). Results: Eleven normal adults (5 males) participated with a mean age of 34.8 ± 7.37 years. Based on an 8 PD threshold, deviations of 8 PD or less demonstrated a sensitivity of 1.0, a specificity of 0.95, and an AUC of 0.97. When categorized into three groups based on 8 PD and 20 PD thresholds, the results were: sensitivity of 0.90 and specificity of 0.95 for ≤ 8 PD; sensitivity of 0.60 and specificity of 1.00 for 8-20 PD; sensitivity of 1.00 and specificity of 0.88 for > 20 PD. Conclusions The machine learning program developed using induced ocular deviations measured with prisms and TPG2 shows promise for use in future strabismus screening tests.

PURPOSE: The purpose of this study was to examine the effects of the mental health promotion program based on Positive Psychology for adolescents with problem behavior. METHODS: The study used a nonequivalent control group pretest-posttest design. Eligible participants were first grade students in two high schools in K Province. The inclusion criteria for the study were those with scores in the upper 15% on the Korean Youth Self-Report. A total of 74 participants were assigned to an 8-session program (n=38) or to a control (n=36) group. The outcome variables were psychological well-being, depression, and self-esteem. RESULTS: There were no statistically significant differences in demographic variables or outcome variables, except self-esteem, between the two groups at the baseline. The experimental group had higher mean scores on psychological well-being and self-esteem and a lower mean score on depression. There were significant differences in psychological well-being (t=3.45, p=.001), self-esteem (F=5.45, p=.022), and depression (t=-2.80, p=.007) between the two groups. CONCLUSION: The mental health promotion program based on Positive Psychology was effective in decreasing depression as well as improving psychological well-being and self-esteem for adolescents with problem behavior. This study contributes to suggesting a framework for promoting mental health for high school students with problem behavior.
Adaptation, Psychological ; Adolescent* ; Depression ; Humans ; Mental Health* ; Problem Behavior* ; Program Evaluation ; Psychology* ; Self Concept

PURPOSE: Based on the results of the relative risk model of Korean breast cancer, the aim of this study was to develop a Korean breast cancer risk assessment tool which would display the absolute breast cancer risks of Korean women. METHODS: The tool was developed in the three steps: selection of risk factors and relative risks, calculation of baseline breast cancer incidences, and estimation of absolute breast cancer risks. The risk factors used in this tool were age, family history of first- and second-degree relatives, body mass index, age at first delivery, history of breast-feeding, and a special test on the breasts. A program was developed in an HTML file, which was used for input of the risk factors, and a CGI file, which was used to calculate the risk and display the results. RESULTS: The program was stored in the Internet web page, http://home.dankook.ac.kr/breast/brca/brca.htm. After receiving an input of risk factors, the program was able to calculate the relative risk compared to all the age groups, the estimated absolute risks following 5 and 10 years, and the estimated absolute risks up to ages 64 and 74 years. The estimated risks of Korean women using this tool were less than those reported by the NCI risk assessment tool. The risk of breast cancer was highest in the fifth decade. CONCLUSION: In this study, we developed a web page containing a Korean breast cancer risk assessment tool. This program may be useful for the assessment of individual breast cancer risks, the selection of screening tools, and the evaluation of preventive options for risk reduction.
Body Mass Index ; Breast ; Breast Neoplasms ; Female ; Humans ; Incidence ; Internet ; Mass Screening ; Risk Assessment ; Risk Factors

PURPOSE: Based on the results of the relative risk model of Korean breast cancer, the aim of this study was to develop a Korean breast cancer risk assessment tool which would display the absolute breast cancer risks of Korean women. METHODS: The tool was developed in the three steps: selection of risk factors and relative risks, calculation of baseline breast cancer incidences, and estimation of absolute breast cancer risks. The risk factors used in this tool were age, family history of first- and second-degree relatives, body mass index, age at first delivery, history of breast-feeding, and a special test on the breasts. A program was developed in an HTML file, which was used for input of the risk factors, and a CGI file, which was used to calculate the risk and display the results. RESULTS: The program was stored in the Internet web page, http://home.dankook.ac.kr/breast/brca/brca.htm. After receiving an input of risk factors, the program was able to calculate the relative risk compared to all the age groups, the estimated absolute risks following 5 and 10 years, and the estimated absolute risks up to ages 64 and 74 years. The estimated risks of Korean women using this tool were less than those reported by the NCI risk assessment tool. The risk of breast cancer was highest in the fifth decade. CONCLUSION: In this study, we developed a web page containing a Korean breast cancer risk assessment tool. This program may be useful for the assessment of individual breast cancer risks, the selection of screening tools, and the evaluation of preventive options for risk reduction.
Body Mass Index ; Breast ; Breast Neoplasms ; Female ; Humans ; Incidence ; Internet ; Mass Screening ; Risk Assessment ; Risk Factors