• Journal of Korean Academy of Nursing
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• v.46 no.3
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• pp.327-337
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• 2016

Purpose: The purpose of this study was to construct and test a structural equation model on resilience of breast cancer patients receiving chemotherapy. Methods: Participants were 204 patients with breast cancer who received chemotherapy treatment. They participated in a structured interview, which included social support, depression, symptom experience, self-efficacy, hope, resilience, and infection prevention behaviors. Data were analyzed using SPSS/WIN 20.0 and AMOS 18.0. Results: Lower depression (${\gamma}=-.33$, p=.020) and symptom experience (${\gamma}=-.31$, p=.012) and higher self-efficacy (${\gamma}=.32$, p=.005) and hope (${\gamma}=.48$, p=.016) were influenced by higher social support. Greater resilience was influenced by lower symptom experience (${\beta}=-.18$, p=.016), higher self-efficacy (${\beta}=.49$, p=.023), and higher hope (${\beta}=.46$, p=.012), and these predictors explained 66.7% of variance in resilience. Greater resilience (${\beta}=.54$, p =.009) made an impact on greater infection prevention behaviors. Resilience mediated the relations of symptom experience (${\beta}=-.10$ p=.013), self-efficacy (${\beta}=.27$, p=.006) and hope (${\beta}=.25$, p=.009) with infection prevention behaviors. These predictors explained 24.9% of variance in infection prevention behaviors. Conclusion: The findings of the study suggest that breast cancer patients with greater resilience who are receiving chemotherapy participate in increased infection prevention behaviors. Further research should be conducted to seek intervention strategies that improve breast cancer patients' resilience.

• Women's Health Nursing
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• v.16 no.2
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• pp.166-176
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• 2010

Purpose: The study was done to examine the effects of cognition-behavior integrated breast cancer prevention education, in which a breast model with interchangeable nodules was utilized, on the self-competency of nursing students in performing breast cancer education. Methods: A nonequivalent control group non-synchronized design was used. A traditional lecture intervention was provided for 49 3rd year college of nursing students (control group) while the integrated breast cancer prevention education was given to 47 3rd year students in the same college one year later (experimental group). The integrated breast cancer prevention education was developed by the research team to strengthen the competency of cognitive and behavioral components in education on breast cancer. Results: Effects of the intervention were found to be significant through all study variables: knowledge about breast cancer (t=7.79, p <.001), breast cancer risk awareness (t=2.05, p <.05), self-competency of breast self-exam (t=8.27, p <.001), and intention to teach breast self-exam (t=3.87, p <.001). Conclusion: The integrated breast cancer prevention education was useful to improve not only knowledge about breast cancer but competency in performing breast examination for nursing students who acquired technical skills from various simulation nodules. As the program helped the students to be prepared as confident educators, future application of the module is recommended for academic curricula.

• Wind and Structures
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• v.28 no.2
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• pp.129-140
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• 2019

Accurately simulating the wind field of large-scale region, for instant urban areas, the locations of large span bridges, wind farms and so on, is very difficult, due to the complicated terrains or land surfaces. Currently, the regional wind field can be simulated through the combination of observation data and numerical model using observation-nudging in the Weather Research and Forecasting model (WRF). However, the main drawback of original observation-nudging method in WRF is the effects of observation on the surrounding field is fully mathematical express in terms of temporal and spatial, and it ignores the effects of terrain, wind direction and atmospheric circulation, while these are physically unreasonable for the turbulence. For these reasons, a spatial correlation-based observation-nudging method, which can take account the influence of complicated terrain, is proposed in the paper. The validation and comparation results show that proposed method can obtain more reasonable and accurate result than original observation-nudging method. Finally, the discussion of wind field along bridge span obtained from the simulation with spatial correlation-based observation-nudging method was carried out.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.868-876
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• 2021

A school zone means a certain section designated among facilities, such as schools, childcare facilities, academies, etc., to protect children from the risk of traffic accidents. Since the school zone was established in September 1995, school zone accidents have not decreased even though it has been strengthened through a total of nine revisions until January 2021. This paper aims to present a standard model for child protection zones based on disaster prevention design. Methods of research included literature research, empirical research, and cognition research. Awareness survey was conducted on children, parents and drivers. Environmental surveys included crosswalks, motorways, pedestrian roads, and traffic lights. The investigation found that visual recognition of school zones by vehicle drivers was difficult, and found that motorcycles using pedestrian roads were a very threat to children. Accordingly, improved school zone standard model design centered on disaster prevention design was presented.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2004.11a
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• pp.268-274
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• 2004

• Korean Journal of Social Welfare
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• v.29
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• pp.68-104
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• 1996

• Journal of Korean Academy of Nursing
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• v.43 no.3
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• pp.442-451
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• 2013

Purpose: This descriptive study was done to develop a predictive model of depression in rural elders that will guide prevention and reduction of depression in elders. Methods: A cross-sectional descriptive survey was done using face-to-face private interviews. Participants included in the final analysis were 461 elders (aged${\geq}$ 65 years). The questions were on depression, personal and environmental factors, body functions and structures, activity and participation. Decision tree analysis using the SPSS Modeler 14.1 program was applied to build an optimum and significant predictive model to predict depression in rural elders. Results: From the data analysis, the predictive model for factors related to depression in rural elders presented with 4 pathways. Predictive factors included exercise capacity, self-esteem, farming, social activity, cognitive function, and gender. The accuracy of the model was 83.7%, error rate 16.3%, sensitivity 63.3%, and specificity 93.6%. Conclusion: The results of this study can be used as a theoretical basis for developing a systematic knowledge system for nursing and for developing a protocol that prevents depression in elders living in rural areas, thereby contributing to advanced depression prevention for elders.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.6981-6986
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• 2015

Background: Breast cancer is the most common cancer in women. One way to decrease the burden of this cancer is early detection through mammography. This study compared the effectiveness of two different educational methods for teachers' uptake of mammography based on the Health Belief Model. Materials and Methods: The current study was a randomised trial of 120 teachers over 40 years old in two groups receiving multimedia or group education, both based on the Health Belief Model. Participants completed questionnaires before, immediately and three months after educational intervention. Mammography was evaluated before and after educational intervention. Results: The participants in the two groups were demographically similar. Comparison showed no difference noted in the scores of knowledge, perceived barriers, susceptibility, and severity constructs between two groups (p > 0.05). Health motivation and benefit were perceived to be higher in the group education compared to the multimedia group. There was a significant difference in mammography between two groups after the intervention (p= 0.003). Conclusions: Planning and implementation of educational program based on the Health Belief Model can raise knowledge and increase participation in mammography especially with group education.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.