• Journal of Korean Physical Therapy Science
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• v.9 no.4
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• pp.63-72
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• 2002

Patient satisfaction is an important factor in evaluating the quality of care. Patient satisfaction may be used to evaluate provider services and facilities, and used to predict the patient returns to a facility. The patients d whether the patient returns to a facility or whether the patient recommends the facility to other people may be affected by a variety of factors of patient satisfaction. Low satisfaction may result in poor compliance with the potential of waste of resources and suboptimal clinical outcome. This study is to identify factors of patient satisfaction that will affect patients decision whether the patient returns or not. A self-administered questionnaire survey was conducted in Seoul, Chung-Joo and Bu-Cheon cities, Survey data was obtained from 743 patients who visited the physical therapy practice at university hospitals, general hospitals and clinics. Response rate was 94.4%. The instrument developed by Goldstein et al. (2000) was used and translated into Korean. Several items were added to the instrument. Patient's opinions of service in each domain measured using 5-point Likert-type scales that ranged from strongly disagree to strongly agree. A multiple-regression analytic approach was used to predict overall satisfaction of physical therapy. Age, kindness, scheduling, convenience of parking, privacy, and waiting time predicted the overall satisfaction of physical therapy. The older patients had higher level of satisfaction with physical therapy compared with the younger patients. Patient satisfaction were more affected by access (scheduling and waiting time), administrative technical management (convenience of parking), and interpersonal management (kindness of physical therapists and other staffs) than clinical technical management (physical therapists' skills).

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.115-132
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• 2004

Recently, as most companies recognize the importance of the customer relationship management, they strongly believe that they must know who their customers are. The job of a customer is very important information for us to understand the customer. However, since most customers are reluctant to reveal them-selves, they do not let us know their jobs, and even provide false information about their jobs. The target domain of our research is mobile telecommunication. In this research, we developed a system that identifies the customer's job by utilizing the Call Detail Record. Using artificial neural networks, we developed a two-step Job Identification System. In the first step, it identifies the four job classes, then in the second step, it subdivides these four job classes into seven jobs. The accuracy of identifying the seven jobs was $71.9\%$.

• Safety and Health at Work
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• v.2 no.2
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• pp.169-175
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• 2011

Objectives: The questionnaire of occupational stress index (OSI) has been popular in the workplace, and it has been tailored for bus drivers in Taiwan. Nevertheless, its outcomes for participants are based on self-evaluations, thus validation by their physiological stress biomarker is warranted and this is the main goal of this study. Methods: A cross-sectional study of sixty-three city bus drivers and fifty-four supporting staffs for comparison was conducted. Questionnaire surveys, 24-hour urine cortisol testing, and blood draws for dehydroepiandrosterone-sulfate (DHEA-S) testing were performed. The measured concentrations of these biological measures were logarithmically transformed before the statistical analysis where various scores of stressor factors, moderators, and stress effects of each OSI domain were analyzed by applying multiple linear regression models. Results: For drivers, the elevated 24-hour urine cortisol level was associated with a worker's relationship with their supervisor and any life change events in the most recent 3 months. The DHEA-S level was higher in drivers of younger age as well as drivers with more concerns relating to their salary and bonuses. Non-drivers showed no association between any stressor or satisfaction and urine cortisol and blood DHEA-S levels. Conclusion: Measurements of biomarkers may offer additional stress evaluations with OSI questionnaires for bus drivers. Increased DHEA-S and cortisol levels may result from stressors like income security. Prevention efforts towards occupational stress and life events and health promotional efforts for aged driver were important anti-stress remedies.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.257-267
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• 2021

Constraints on the structure and composition of the active layer are important for understanding permafrost evolution. Soil convection owing to repeated moisture-induced freeze-thaw cycles within the active layer promotes the formation of self-organized patterned ground. Here we present the results of ground penetrating radar (GPR) surveys across a selected sorted circle near King Sejong Station, Antarctica, to better delineate the active layer and its relation to the observed patterned ground structure. We acquire GPR data in both bistatic mode (common mid-points) for precise velocity constraints and monostatic mode (common-offset) for subsurface imaging. Reflections are derived from the active layer-permafrost boundary, organic layer-weathered soil boundary within the active layer, and frozen rock-fracture-filled ice boundary within the permafrost. The base of the imaged sorted circle possesses a convex-down shape in the central silty zone, which is typical for the pattern associated with convection-like soil motion within the active layer. The boundary between the central fine-silty domain and coarse-grained stone border is effectively identified in a radar amplitude contour at the assumed active layer depth, and is further examined in the frequency spectra of the near- and far-offset traces. The far-offset traces and the traces from the lower frequency components dominant on the far-offset traces would be associated with rapid absorption of higher frequency radiowave due to the voids in gravel-rich zone. The presented correlation strategies for analyzing very shallow, thin-layered GPR reflection data can potentially be applied to the various types of patterned ground, particularly for acquiring time-lapse imaging, when electric resistivity tomography is incorporated into the analysis.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.73-94
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• 2008

When individuals collaborated in virtual settings, communication is medicated through a variety of communication technologies, and is associated not only with communication effectiveness but also with socio-emotional interactions among group members. In this regards, scholars have examined how technology-mediated communication systems can be designed and used to facilitated communication interaction. However, the empirical results of the previous studies have revealed inconsistencies in the effects of communication media on users' behavioral or attitudinal responses, and on their viable effectiveness in organizations. Some studies claim that computer-mediated communication(CMC) is task-oriented but not suitable for emotional expression since it hinders close interpersonal interaction. On the other hand, some studies argue that individuals are able to develop interpersonal relationships more effectively in a CMC environment than in an FtF-environment. Due to the different perspectives, a theoretical gap exists, and it leads to the inconsistent research findings. The purpose of this paper is to combine the two different perspectives into single unified model, thereby providing a more realistic and comprehensive understanding about virtual collaboration. The present study here sought to answers the following questions with organizational communication perspective: What are the major components of virtual collaboration? What factors affect the performance of virtual collaboration? And what kind of managerial efforts should organization make in order to facilitate CMC media effectiveness in virtual collaboration? Although there is a certain belief that new media, namely technology-mediated communication support would create new opportunities, the problem of "how" or "why" has been an important question that is still not fully addressed. In this regards, we collectively reexamined previous literatures with major issues which are still controversial and integrated various theoretical activity within computer-mediated communication domain: task-oriented approach, socio-emotional approach, and evolutionary psychological approach. Our first contribution is to develop a framework for virtual collaboration by combining two different perspectives into a single unified model, providing a more realistic and comprehensive understanding. The second main contribution is the joint modeling of both social presence and cognitive effort, and the effects on two distinct but important communication outcomes(i.e., take performance and relational development). We tested the research hypotheses which were developed based on the various CMC theories using data gathered through a self-administered mail survey of 127 individuals of 69 virtual workgroups. The proposed model was supported, providing preliminary evidence that the tension between two opposite view should be integrated. The results show that the individual's psychological processes(social presence and cognitive effort) in a virtual environment significantly mediated the effect of CMC inputs (media richness, user adaptation, and shared contest) on the CMC outputs (task performance and relational development). Furthermore, this study shows that the lack of perceived media richness of CMC media can be complemented by user adaptation and shared context. Based on the results, we discuss how communication system should be designed and implemented so as to promote virtual interaction as well as how a virtual workgroup should be composed to complement the lack of media richness. A virtual collaboration using CMC media may create new value by overcoming the logistical constraints. On the other hand, it may also generate various managerial risks such as communicational depersonalization, process dissatisfaction, and low cohesion. Therefore, this study suggests that organization managers should carefully choose the CMC mediums and monitor individual member's cognitive and affective psychological processes during virtual collaboration to reduce potential risks in virtual collaboration.

• Journal of Hospice and Palliative Care
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• v.17 no.4
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• pp.259-269
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• 2014

Purpose: This preliminary study is aimed at developing standardized tools for psycho-social assessment of patients in needs for hospice/palliative care. To accomplish the purpose, investigators examined effects of perceptions of social workers on the importance of psycho-social domains of assessment in hospice/palliative care settings. Moreover, investigators paid attention to variances of perceptions of social workers' along with types of institution and credentials of those family settings. Methods: A form of questionnaire was first explored from an initial interview assessment of 10 government-certified hospice care providers and a literature review, second constructed with eight domains and 80 items, and sent by e-mail to 55 institutions and hospitals providing hospice/palliative cares in Korea. Lastly, a total of 31 agencies returned with a completed responses and consent form (56% response rate). SPSS program (version 18.0) was used for data analysis. Results: Study found that social workers perceived patients' family background (m=4.53, 5-point scale) as the most important assessment domain, whereas economic conditions (4.06 point) the least important. Social workers' perception varied by credentials (i.e., license types, training, full-time position, types of care facility). Conclusion: Based upon study findings, investigators can conclude strong needs for developing a assessment tool that measures multiple domains (i.e., psychological, social and ecological aspects) of patients. A standardized assessment tool should be structured with 2 axis (center/core and expanded/peripheral) and tailored for institution type. Second, professional trainings must be provided by strengthening legal institutionalization and fostering qualified social workers with full responsibilities of hospice and palliative care patients.

• Korean Journal of Health Education and Promotion
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• v.20 no.4
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• pp.95-109
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• 2003

Tobacco use is the most readily preventable cause of premature death； it is a worldwide problem, with a significant impact on heath and well-being. In order to design an effective tobacco education program, it is important to understand smoking patterns and the underlying factors associated with smoking in different generations such as adults or young people. Despite a general decline in the prevalence of regular smoking among adults, recent surveys commissioned by the Ministry Heath ＆ Welfare for the Republic of Korea have shown no evidence of any decline in smoking rates among young women and adolescents. The Republic of Korea has the highest adult male smoking percentage (65.1%) in the world and smoking in adolescents is still an increasing trend. Smoking in adolescents and young women is especially more dangerous, thus health education of anti-smoking directed at these groups is an important area that will benefit from using internet content that they can easily access. The purpose of this study is the evaluation of visual perception and effectiveness analysis in smoking cessation websites in promoting smoking cessation in adolescents and young women through Internet content. As a result of this project, at first we evaluated the Internet content of cyber smoking cessation programs by the evaluation criteria of web design interface. The Internet site of http：//nosmokeguide.or.kr received the most superior evaluation in the domestic Internet content for smoking cessation and the Internet site of the National Center for Tobacco-Free Kids received the most superior evaluation in the foreign Internet content for smoking cessation. This evaluation was surveyed by an expert in Internet content and user. Secondly, we developed the Internet content for cyber smoking cessation program, namely, "Dr. Smoking" that contained several menus and a database regarding anti-smoking designed in accordance with the results of this evaluation. The domain address of Dr. Smoking is http：//www.dmosmoking.com and our webpage has assorted kinds of news, information, self-diagnosis, prescription, consulting, a no-smoking mall etc. In conclusion, this project is designed to develop Internet content for the most effective smoking cessation program and to contribute to eliminating smoking from our society. We also will try to develop and upgrade this web-site in order to help a smoker who want to quit smoking and diminish the physical and socioeconomic harm from smoking.m smoking.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.222-228
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• 2020

The purpose of this study was to investigate activity participation level and functional disability based on ICF for the elderly aged over 65 years. Subjects were 100 senior citizens. We performed data collection using Korean Activity Card Sorting(KACS) and World Health Organization Disability Assessment Schedule 2.0(WHODAS 2.0). Data were analyzed using descriptive statistics, Pearson's correlation, multiple regression. As a result of the survey of activity participation levels, retained level of activity participation of instrumental activity was highest at 75.06%. Among the WHODAS 2.0 sub-domain, 'getting along with people', 'participation in society' had the most difficulties and 'self-care', 'life activities' were the lowest. An analysis of the correlation between the activity retention rate and functional disability showed that there was a significant negative correlation. Significant factors influencing functional disability were activity participation level of social activity, instrumental activity and main work(retirement). We confirmed that activity participation level was important factor on functional disability. Further, we need standardization study for generalization.