• Journal of Technologic Dentistry
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• v.42 no.4
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• pp.379-387
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• 2020

Purpose: This study aimed to explore methods to improve the quality recognition and satisfaction level of non-face-to-face lectures among students in the departments of dental technology and dental hygiene. Methods: This survey was conducted to assess the status and preference of non-face-to-face lectures and the level of awareness and satisfaction regarding the quality of these lectures among 179 students of dental technology and 295 students of dental hygiene. Statistical analyses were performed using frequency analysis, independent sample t-test, one-way ANOVA (post-hoc Duncan), Welch analysis (post-hoc Games-Howell), and hierarchical multiple regression analysis. Results: Factors that affected the ability to assess the quality of non-face-to-face lectures were the department, the method of non-face-to-face lectures, the most preferred method for conducting lectures, the level of awareness regarding the quality of face-to-face lecture, and satisfaction level. It has 71.5% explanatory power. Moreover, factors that influenced the satisfaction level of non-face-to-face lectures included the department, grade, the highest satisfied non-face-to-face teaching method, the most effective theoretical non-face-to-face teaching method, the most preferred teaching methods, and the ability to assess quality of face-to-face lectures. It has 46.8% explanatory power. Conclusion: Non-face-to-face classes should be designed and developed for web-based programs to improve the motivation and achievement level of the students and encourage interaction between the professors and students. Our findings suggest that educators should strive to achieve optimal educational effects by efficiently combining face-to-face and non-face-to-face lectures.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.271-282
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• 2021

This study was conducted to develop a model for predicting the length of stay for premature infants through machine learning. For the development of this model, 6,149 cases of premature infants discharged from the hospital from 2011 to 2016 of the discharge injury in-depth survey data collected by the Korea Centers for Disease Control and Prevention were used. The neural network model of the initial hospitalization was superior to other models with an explanatory power (R²) of 0.75. In the model added by converting the clinical diagnosis to CCS(Clinical class ification software), the explanatory power (R²) of the cubist model was 0.81, which was superior to the random forest, gradient boost, neural network, and penalty regression models. In this study, using national data, a model for predicting the length of stay for premature infants was presented through machine learning and its applicability was confirmed. However, due to the lack of clinical information and parental information, additional research is needed to improve future performance.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• Journal of Korean Clinical Nursing Research
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• v.28 no.3
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• pp.211-222
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• 2022

Purpose: This study aimed to identify the effects of preparedness towards delegation, critical thinking disposition, and communicative competence on job satisfaction among comprehensive nursing care service ward nurses and general ward nurses. Methods: The participants in this study were 218 nurses from two university hospitals. Data were collected using self-administered questionnaires and were analyzed using SPSS Statistics/WIN 21.0, including descriptive statistics, x² test, Fisher's exact test, t-test, ANOVA, Scheffeé test, Pearson's correlation coefficient, and multiple linear regression analysis. Results: For comprehensive nursing care service ward nurses, the significant influencing factors on job satisfaction were age (β=-.37, p=.041), marital status (β=.35, p=.001), critical thinking disposition (β=.29, p=.003), and communicative competence (β=.28, p=.002) with the explanatory power was 39.0% (F=12.00, p<.001). On the other hand, for general ward nurses, the significant influencing factors on job satisfaction were advanced degree (β=.29, p=.047), critical thinking disposition (β=.23, p=.040), and communicative competence (β=.42, p<.001) with the explanatory power was 33.0% (F=11.78, p<.001). Conclusion: The results of this study suggest the importance of developing and applying education programs to improve critical thinking disposition and communicative competence of nurses. This would enhance the comprehensive nursing service and increase overall nursing manpower resources. In addition, further studies are needed to improve nursing work environments and to develop strategies for enhancing preparedness towards delegation, which would increase the job satisfaction of nurses in the comprehensive nursing care service wards.

• Journal of Korean Public Health Nursing
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• v.36 no.3
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• pp.361-374
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• 2022

Purpose: This study applies an ecological model to investigate individual and organizational levels to identify factors influencing the HRQOL of industrial employees. Methods: Totally, 133 industrial workers of a vehicle company were enrolled, who understood the purpose and consented to participate in the study. The collected data were analyzed by frequency, percentage, mean, standard deviation, independent t-test, one-way ANOVA, Scheffe Test and hierarchical regression analysis using the SPSS 20.0 program. Results: Hierarchical regression analysis showed that job Stress(β=-.44, p<.001), and hobbies(β=-.21, p=.013) were the major influencing factors of the Physical Component Summary of HRQOL, which had an additional explanatory power of 11.5%. The influencing factors for the Mental Component Summary of HRQOL were job stress(β=-.43, p<.001), and coronary artery disease(β=.17, p=.034) with an additional explanatory power of 13.5%. Conclusion: Results of this study, reveal that a multidimensional approach based on an ecological model is suitable as a health promotion intervention strategy to improve the HRQOL. We further propose developing a multi-dimensional health promotion program that consider the individual and organizational factors such as job stress, activation of in-house clubs, and assessing and managing of the risk of cerebral and cardiovascular diseases.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.161-165
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• 2022

The purpose of this study was to improve children's positive perceptions of the future by examining the relationship between parental attachment, parental involvement in learning, and children's perceptions of the future, and by identifying the specific influence of each variable on children's perceptions of the future. Frequency analysis and descriptive statistical analysis were performed on data from the 12th year of the Korean Children's Panel (2019), and Pearson's moment correlation coefficient was calculated for correlation analysis between variables. Multiple regression analysis was performed to examine the explanatory power of parental attachment to children's perception of the future and parental participation in learning. The research results are as follows. First, the correlations among all the latent variables of parental attachment, parental participation in learning, and children's future perception showed significant correlations. Second, the explanatory power of children's perception of the future was found in the order of 'mother' trust, 'family'-based participation, 'father' trust, and 'mother' communication. These results suggested that parental trust and warm, warm participation in home-based learning were important variables in children's positive perception of the future.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.53-62
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• 2022

Purpose : This research is a descriptive study that aimed to identify the levels of health promotion behaviors, fatigue, and depression of nursing students and confirm the effect of health promotion behaviors on fatigue and depression. Methods : Data were collected from September 27 to October 15, 2021 through survey questionnaires on 178 nursing students of 2 universities in G-do who voluntarily agreed to participate. The collected data were analyzed using SPSS 23.0, the general characteristics of nursing students were analyzed by frequency and percentage, and the levels of their health promotion behaviors, fatigue, and depression of nursing students was analyzed by mean and standard deviation. The relationship among the health promotion behaviors, fatigue, and depression was analyzed by Pearson's Correlation Coefficient. Multiple regression analysis was performed for the effects of health promotion behaviors on fatigue and depression of nursing students. Results : The nursing students' health promotion behaviors averaged 2.25±.43 points out of 5, and for each sub-factor, interpersonal relationship was the highest at 2.66±.49 points and physical activity was the lowest at 2.01±.60 points. Fatigue was 4.89±1.02 points out of 7 and depression was 10.52±9.10 points out of 63. A statistically significant correlation was found among health promotion behaviors, fatigue, and depression. Health promotion behaviors that significantly affected fatigue were stress management (𝛽=-.263, p=.004) and physical activity (𝛽=-.208, p=.026), which showed 35 % explanatory power on fatigue. Health promotion behaviors that significantly affected depression were spiritual growth (𝛽=-.342, p=.002), and physical activity (𝛽=-.231, p=.016), which showed 31 % explanatory power for depression. Conclusion : This study is meaningful in that it provided basic data to develop an effective health promotion behavior program to prevent and manage nursing students' fatigue and depression, by identifying and analyzing the sub-factors of health promotion behaviors affecting their fatigue and depression.

• Journal of the Korean Geosynthetics Society
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• v.21 no.2
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• pp.21-30
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• 2022

Shear strength is the most important indicator in the evaluation of rock slope stability. It is generally estimated by comparing the results of existing literature data, back analysis, experiments and etc. There are additional variables related to the state of discontinuity to consider in the shear strength of the rock slope. It is difficult to determine whether these variables exist through drilling, and it is also difficult to find an exact relationship with shear strength. In this study, the data calculated through back analysis were used. The relationship between previously considered variables was applied to deep learning and the possibility for estimating shear strength of rock slope was explored. For comparison, an existing simple linear regression model and a deep learning algorithm, a deep neural network(DNN) model, were used. Although each analysis model derived similar prediction results, the explanatory power of DNN was improved with a small differences.

• Journal of Home Health Care Nursing
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• v.30 no.2
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• pp.163-173
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• 2023

Purpose: The aim of this descriptive research study was to identify the factors affecting the ethical decision-making of nursing students. Methods: A convenience sample of 193 nursing students from three nursing colleges in D city who were engaged in clinical practice completed an online Google Forms questionnaire from September 9 to September 20, 2021. Using SPSS 23.0, data were analyzed with descriptive statistics, an independent t-test, a one-way ANOVA, Scheffé's test, Pearson's correlation coefficient, and a multiple regression analysis. Results: The influencing factors of ideal ethical decision-making were guilt (β=.38, p<.001), awareness of the nurses' Code of Ethics (β=.18, p=.023) and motivation for entering school, among general characteristics (β=-.18, p=.033). The explanatory power of the model was 22.2%. Further, the influencing factors of realistic ethical decision-making were ideal ethical decision-making (β=.26, p=.001) and grade (among general characteristics) (β=.15, p=.029); the explanatory power of the model was 17.9%. Conclusion: Various educational tools and programs pertaining to making ideal and ethical decisions must be enhanced to promote ethical choices in clinical areas and realistic ethical decision-making ability to actually make such choices. This focus may enable nurses to improve their nursing professionalism in the future.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.149-158
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• 2022

This study was conducted to understand the effects of openness, positive acceptance, physical symptoms, reaction, and emotional sensitivity among adolescents' attitudes toward menstruation, and academic stress on premenstrual syndrome and dysmenorrhea. A questionnaire of 126 high school girls who agreed to participate in the study was analyzed. As a result, the subject's premenstrual syndrome score was 22.48 points and dysmenorrhea was 4.66 points, and it was found that the higher the amount of menstruation, the higher the score of premenstrual syndrome and dysmenorrhea. Factors affecting premenstrual syndrome were openness, physical symptoms, reaction, and emotional sensitivity, and the explanatory power of these variables was 49.9%. In addition, positive acceptance and physical symptoms had a significant effect on dysmenorrhea in adolescents, and explanatory power was 66.3%.