• Journal of Korean Academy of Nursing
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• v.25 no.3
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• pp.581-596
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• 1995

Clinical practice in nursing education provides an opportunity for students, through the process of ap-plying theoretical knowledge to practice, and to learn nursing skills as well as being socialized into nursing and as such decrease the reality shock of actual nursing practice. Because of a shortage of nursing faculty, the job of achieving the objectives of the clinical practice had been turned over to the head nurses. This resulted in many problems, such as, unclear location of responsibilities and inadequate feedback from head nurses. Therefore this study was done to introduce and evaluate the use of preceptors as a way to minimize the above problems, and to maximize the achievement of the clinical practice objectives. Using an adaptation of Zerbe's (1991) three-tiered team model, clinical practice was done using a preceptor, a head nurse and a clinical instructor, each with different and well defined roles. The subjects of this study were 67 senior students of the College of Nursing of Y University in Seoul whose clinical practice in adult nursing was carried out between May 1, 1994 and December 8, 1994. There were 22 preceptors who had at least two years of clinical experience and who were recommended by their head nurses. They were given additional education on the philosophy and objectives of the College of Nursing, on communication skills, on the theory and practice of education, and on nursing diagnosis and education evaluation. The role of the preceptor was to work one-to-one with students in their practice. The role of the head nurse was to supervise and evaluate the preceptors. The role of the clinical instructor was to provide the education program for the preceptors, to provide ad-vice and suggestions to the preceptors and to maintain lines of communication with the college. With each of these roles in place, it was thought that the effectiveness and efficiency of the clinical practice could be increased significantly. To evaluate the effectiveness of the preceptorship, the three - tiered model, Lowery's Teacher Evaluation Opinion Form translated and adapted to Korea was used to measure student statisfaction. The Clinical Practice Compentency Evaluation Tool developed by Lee et ai was also used to measure student competencies. The results of this study are as follows 1. The satisfaction with clinical practice was higher with the introduction of the perceptors than it was before they were used. (t=-5.96, p=<.005) 2. The clinical practice competencies were higher with the introduction of the preceptors than it was before they were used(t=-5.l3, p<.005) 3. In order to analyze areas not measured by the quantitative tools additional analysis of the open questions was done. The results of this analysis showed that : 1) The students felt positive about their sense of security, confidence, handling of responsbility, and being systematic. They also felt positive about improvements in knowledge, opportunities for direct care, and socialization. 2) The students felt negative about the technical part of their role, lack of knowledge by the preceptor, unprofessional attitudes on the part of the preceptor, difficulty in the role of the professional nurse(student). 3) The preceptors felt positive about their responsibility, motivation, and relationship with the college. 4) The preceptors felt negative about their bur-den. Introduction of the preceptorship model will lead to change and improvement in the negative factors discussed above, solve problems in the present clinical education system, increase continuity in the education of the students, help with socialization of the students and motivation of the preceptors to up-grade their education and increase their confidence. These objectives must be obtained to further the development of professional nursing, and thus, making the preceptorship a reality is our job for the future.

• 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 Service Research and Studies
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• v.13 no.3
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• pp.172-189
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• 2023

This study investigated the potential contribution of ChatGPT, a massive language and information model, in the decision-making process of public design policies, focusing on the characteristics inherent to public design. Public design utilizes the principles and approaches of design to address societal issues and aims to improve public services. In order to formulate public design policies and plans, it is essential to base them on extensive data, including the general status of the area, population demographics, infrastructure, resources, safety, existing policies, legal regulations, landscape, spatial conditions, current state of public design, and regional issues. Therefore, public design is a field of design research that encompasses a vast amount of data and language. Considering the rapid advancements in artificial intelligence technology and the significance of public design, this study aims to explore how massive language and information models like ChatGPT can contribute to public design policies. Alongside, we reviewed the concepts and principles of public design, its role in policy development and implementation, and examined the overview and features of ChatGPT, including its application cases and preceding research to determine its utility in the decision-making process of public design policies. The study found that ChatGPT could offer substantial language information during the formulation of public design policies and assist in decision-making. In particular, ChatGPT proved useful in providing various perspectives and swiftly supplying information necessary for policy decisions. Additionally, the trend of utilizing artificial intelligence in government policy development was confirmed through various studies. However, the usage of ChatGPT also unveiled ethical, legal, and personal privacy issues. Notably, ethical dilemmas were raised, along with issues related to bias and fairness. To practically apply ChatGPT in the decision-making process of public design policies, first, it is necessary to enhance the capacities of policy developers and public design experts to a certain extent. Second, it is advisable to create a provisional regulation named 'Ordinance on the Use of AI in Policy' to continuously refine the utilization until legal adjustments are made. Currently, implementing these two strategies is deemed necessary. Consequently, employing massive language and information models like ChatGPT in the public design field, which harbors a vast amount of language, holds substantial value.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.