• Journal of Korean Academy of Fundamentals of Nursing
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• v.10 no.2
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• pp.206-213
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• 2003

Purpose: This study was designed to explore pain and self-care behaviors and identify related variables in patients with chronic arthritis. Method: One hundred fifty patients with arthritis were recruited from two university based arthritis centers according to selection criteria. Collected data were analyzed using the SAS program to analyze the responses to the structured questionnaires of the study. Result: Most of the participants expressed pain and the intensity of the pain was moderate. There were significant differences according to age, educational level, diagnosis, duration of illness, number of affected joint, and use of complementary therapy. Self-care behavior scores were moderately high. The highest practice was for 'regular visits to the hospital', and the lowest for 'applying physical therapy at home'. The mean self-care behavior scores showed significant differences according to economic status and educational level. Pain scores showed no correlation with self-care behavior. Conclusion: Developing self-management programs for patients with chronic arthritis should focus on self-care skills which are applicable in the relief of pain and enhancement of knowledge. The skills are recommended not only for better health practices but also for enhancing the level of well-being and life satisfaction.

• Child Health Nursing Research
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• v.15 no.2
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• pp.219-227
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• 2009

Purpose: This study was done to evaluate depression in child cancer survivors. Methods: The participants in this descriptive research were 103 adolescents who agreed to participate. All of the adolescents were diagnosed as completely cured but remained under follow-up observation as outpatients. A structured questionnaire including the depression scale developed by Shin et al. (1991), a self-esteem scale developed by Rosenberg (1965) and a social support scale developed by Kim & Park (1999), Lee (1997) were used for data collection. The data were analyzed using SPSS. Results: The mean scores were, for self-esteem, 36.01, for paternal support, 57.21, for maternal support, 59.98, for peer support, 22.19, for teachers support, 21.07 and for depression, 27.95. Scores for depression were significantly different according to academic achievement, satisfaction with appearances, diagnosis and prognosis. Depression was negatively correlated with self-esteem, paternal support, maternal support, peer support, and teachers support. Variables affecting depression were peer support and self-esteem, accounting for 47.0% of the variance. Conclusion: The findings indicate that peer support and self-esteem, follow by maternal, paternal and teachers support, are important variables in the occurrence of depression in these adolescents. Further study is needed to develop strategies to increase this support and self-esteem.

• Korean Journal of Adult Nursing
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• v.13 no.1
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• pp.70-81
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• 2001

The relationships among self efficacy, depression and coping with cancer were examined in 194 outpatients who had received a diagnosis of cancer. The sample for this descriptive correlational study consisted of people who were at least 19 years old and had been treated for cancer at 6 hospital in Seoul. Data were collected using a self-report questionnaire. The results of this study are as follows: 1. People who attributed cancer to heredity/family showed the highest mean score of self efficacy. People who attributed cancer to smoking showed the highest mean score of depression. and coping. 2. There were significant differences between causal attribution and depression and between causal attribution and coping. 3. There was a negative correlation between self-efficacy and depression(r=-.301, p= .000), whereas there was a positive correlation between self-efficacy and coping (r=.195, 0=.006). Finally, it is evident that identifying clear perceived causes, self-efficacy, depression and coping in patients with cancer continues to challenge researchers. Based upon this study, it is recommended that future research have a longitudinal design that allows for the identification of changes in perception, emotion and coping and, possibly, different relationships over time.

• Perspectives in Nursing Science
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• v.11 no.1
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• pp.39-48
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• 2014

Purpose: This study investigated the effects of an education program integrating self-efficacy theory and narratives on self-efficacy, knowledge, and resilience in women with breast cancer. Methods: This study employed a nonequivalent control group posttest only design. A 3-day program consisting of sessions in which participants shared their experiences of breast cancer, lectures on breast cancer, and breast self-examinations was implemented. Data were collected using self-reported questionnaires in 2013. Results: The mean age of participants was $50.8{\pm}5.3$; approximately half (52.8%) had Stage II breast cancer at the time of diagnosis. The results showed that the levels of self-efficacy, knowledge, and resilience were significantly higher in the experimental group than in the control group (p<.05). Conclusion: The results of the study suggest that programs integrating self-efficacy theory and narratives would be effective in promoting resilience as well as self-efficacy and knowledge in women with breast cancer. Further studies are needed to identify the effects of such education programs for people with other types of cancer or chronic illnesses.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.413-422
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• 2002

In a power plant, disturbance detection and diagnosis are massive and complex problems. Once a disturbance occurs, it can be either persistent, self cleared, cleared by the automatic controllers or propagated into another disturbance until it subsides in a new equilibrium or a stable state. In addition to the Physical complexity of the power plant structure itself, these dynamic behaviors of the disturbances further complicate the fault monitoring and diagnosis tasks. A data structure called a disturbance interrelation analysis graph(DIAG) is proposed in this paper, trying to capture, organize and better utilize the vast and interrelated knowledge required for power plant disturbance detection and diagnosis. The DIAG is a multi-layer directed AND/OR graph composed of 4 layers. Each layer includes vertices that represent components, disturbances, conditions and sensors respectively With the implementation of the DIAG, disturbances and their relationships can be conveniently represented and traced with modularized operations. All the cascaded disturbances following an initial triggering disturbance can be diagnosed in the context of that initial disturbance instead of diagnosing each of them as an individual disturbance. DIAG is applied to a typical cooling water system of a thermal power plant and its effectiveness is also demonstrated.

• Korean Journal of Human Ecology
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• v.12 no.5
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• pp.605-618
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• 2003

The purpose of this study was to develop the household analysis and diagnosis program for the enhancement of family welfare focusing on the household of middle-aged full-time housewives. From the review of related literatures, the following areas of total family life were selected ; household management, time use, housing, household materials, finance, communication and decision making. Considering that the program subjects were middle-aged full-time housewives, the preparation for elderly life and the identity as housewife, one of the healthy family members, are appended. The progresses for the development of household analysis and diagnosis program were as follows: 1. to select the useful area of family life, 2. to make the indicators which explain the status of family life, and 3. to decide the scales for the diagnosis. This program has various uses such as the development of self-evaluation program, program for various family life course. Through this program the strength and weakness of family life can be found and the planning for the enforcement of family life can be practiced.

• Journal of Society of Preventive Korean Medicine
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• v.26 no.2
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• pp.25-35
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• 2022

Objectives : This study aims to analyze the patents filed in the clinical diagnosis sector where technologies have been actively developed since the advent of the 4th industrial revolution. Methods : The analysis has been conducted in two ways - the period from 2016 to 2021 and the time points before and after COVID-19 - by visualizing based on the word cloud method. Results : Over two thirds of patents has been filed in the A61B sector (71.8%) and cure, sensor, self diagnosis, control, and breakdown have been observed in the period above. During the overall period (2016~2021), 'ultrasound'(7.5%), 'image'(5.1%), 'skin'(4.0%), 'treatment'(3.4%), and 'artificial intelligence(2.5%)' were the frequently patent applications technologies. In addition, 'ultrasound'(6.2%), 'image'(5.5%), 'skin'(4.0%), 'treatment' (3.7%), and 'portable'(1.7%) appeared most frequently before COVID-19 whereas 'ultrasound(5.5%)', 'artificial intelligence(4.2%)', 'diagnostic device'(1.9%), 'dimentia'(1.6%), and 'diagnostic kit'(1.4%) emerged the most after COVID-19. Conclusion : This study is meaningful in that it showed the technological development trend in the digital diagnosis sector and it was found that the Korean medicine field should contribute to this field more actively in the future.

• Current Optics and Photonics
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• v.7 no.3
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• pp.283-296
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• 2023

An intraoral spectral domain optical coherence tomography (SD-OCT) system has been developed, using a custom-built hand-held scanner and spectrometer. The hand-held OCT probe, based on a microelectromechanical systems scanner and a self-built miniaturized drive circuit, had a field of view sufficient for dental diagnosis. The spectrometer using a fabricated f-theta lens provided the image depth required for dental diagnosis. The axial and transverse resolutions of the OCT system in air were 7.5 ㎛ and 12 ㎛ respectively. The hand-held probe could scan an area of 10 × 10 mm², and the spectrometer could image along a depth of 2.5 mm. To verify the utility of the developed OCT system, OCT images of tooth hard and soft tissues were acquired, and a user-interface program for diagnosis was developed. Early caries and microcracks that were difficult to diagnose with existing methods could be found, and the state of restoration could be observed. Measuring the depth of the gingival sulcus, distinguishing subgingival calculus, and detecting an implant under the gingiva suggested the possibility of the SD-OCT system as a diagnostic for dental soft tissues. Through the presented OCT images, the capability of the developed SD-OCT system for dental diagnosis was demonstrated.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.262.2-262.2
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• 2014

Plasma processing is an essential process for pattern etching and thin film deposition in nanoscale semiconductor device fabrication. It is necessary to maintain plasma chamber in steady-state in production. In this study, we determined plasma chamber state with residual gas analysis with self-plasma optical emission spectroscopy. Residual gas monitoring of fluorocarbon plasma etching chamber was performed with self-plasma optical emission spectroscopy (SPOES) and various chemical elements was identified with a SPOES system which is composed of small inductive coupled plasma chamber for glow discharge and optical emission spectroscopy monitoring system for measuring optical emission. This work demonstrates that chamber state can be monitored with SPOES and this technique can potentially help maintenance in production lines.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.453-461
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• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.