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The Prediction of Export Credit Guarantee Accident using Machine Learning (기계학습을 이용한 수출신용보증 사고예측)

  • Cho, Jaeyoung;Joo, Jihwan;Han, Ingoo
    • Journal of Intelligence and Information Systems
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    • v.27 no.1
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    • pp.83-102
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    • 2021
  • The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

Development of the Accident Prediction Model for Enlisted Men through an Integrated Approach to Datamining and Textmining (데이터 마이닝과 텍스트 마이닝의 통합적 접근을 통한 병사 사고예측 모델 개발)

  • Yoon, Seungjin;Kim, Suhwan;Shin, Kyungshik
    • Journal of Intelligence and Information Systems
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    • v.21 no.3
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    • pp.1-17
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    • 2015
  • In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

The Effect of Marketing Characteristic on Business Performance (창업마케팅특성이 기업성과에 미치는 영향)

  • Jeon, In-oh;An, Un-Seok
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.11 no.3
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    • pp.97-109
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    • 2016
  • In Korea, the survival rate of start-up of 5-year after foundation is as low as 29.6% of the country. This low survival rate is from because of insufficient resources in start-ups compared to those of mid-sized companies. Therefore, the marketing characteristics of entrepreneurship has emerged as a major cause. Therefore, In this study, because learning orientation, marketing experience, competition orientation and etc are differently owned in start-ups, marketing impact to marketing strategy in start-up companies are differently investigated. Therefore, the relationship of learning orientation, marketing experience, competition Orientation with marketing strategies was examined. Based on this, Business performance was examined to suggest contents related to eco-system of start-up companies to representative of start-up companies. For this study, Survey was conducted for 250 start-up entrepreneurs within 3 and half year since foundation from Nov. 20 to Dec. 20, 2015. In result of data-cleaning, 207 meaningful samples were gathered. Based on these, conclusion was obtained. Using SPSS 20.0 statistical program, frequency analysis, reliability analysis, correlation analysis and regression analysis were conducted. the following conclusions were drawn. First, in the impact of marketing environment of Phase 1 start-up companies on marketing strategy, product strategy, distribution strategy and promotion strategy were positively affected by learning orientation, marketing experience and competition orientation. Second, in the effect of 2nd phase marketing strategy to business performance, the financial performance and the non-financial performance. Were positively affected by product strategy, distribution strategy and promotion strategies. Third, The effect of learning orientation, marketing experience and competition orientation to financial performance was positively mediated by product strategy and distribution strategy among 3rd phase meditation strategies. the effect of learning orientation, marketing experience and competition orientation to non-financial performance was positively mediated by products strategy. In comprehensive summary, in order to increase business performance in start-up companies, marketing strategy should be applied in. Especially, the role of learning orientation and marketing experience is vital. In increasement of business performance to characteristics of star up marketing, financial performance can be increased by product strategy and distribution strategy. And, both of financial and non-financial performance can be increased by product strategy. Therefore, in conducting of marketing characteristics of start-up, to increase business performance, the apply of marketing strategy to marketing characteristics of start-up should be required.

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Optimal Incentives for Customer Satisfaction in Multi-channel Setting (멀티채널에서의 고객만족제고 인센티브 연구)

  • Kim, Hyun-Sik
    • Journal of Distribution Research
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    • v.15 no.1
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    • pp.25-47
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    • 2010
  • CS is one of the major concerns of managers in the world because it is well known to be a key medium construct for firms' superior outcome. One of the major agents for CS management is retailers. Firms try to manage not only employees but also retailers to promote CS behaviors. And so diverse incentives are used to promote their CS behaviors under diverse channel setting such as multi-channel. However in spite of the rising needs there has been scarce studies on the optimal incentive structure for a manufacturer to offer competing retailers at the multi-channel. In this paper, we try to find better way for a manufacturer to promote the competing retailers' CS behaviors. We investigated how to promote the retailers' CS behavior via game-theoretic modeling. Especially, we focus on the possible incentive, CS bonus type reward introduced in the studies of Hauser, Simester, and Wernerfelt(1994) and Chu and Desai(1995). We build up a multi stage complete information game and derive a subgame perfect equilibrium using backward induction. Stages of the game are as following. (Stage 1) Manufacturer sets wholesale price(w) and CS bonus($\eta$). (Stage 2) Both retailers in competition set CS effort level($e_i$) and retail price($p_i$) simultaneously. (Stage 3) Consumers make purchasing decisions based on the manufacturer's initial reputation and retailers' CS efforts.

    Structure of the Model We investigated four issues about the topic as following: (1) How much total incentive is adequate for a firm of a specific level of reputation to promote retailers' CS behavior under multi-channel setting ?, (2) How much total incentive is adequate under diverse level of complimentary externalities between the retailers' CS efforts to promote retailers' CS behavior?, (3) How much total incentive is adequate under diverse level of cost to make CS efforts to promote retailers' CS behavior?, (4) How much total incentive is adequate under diverse level of competition between retailers to promote retailers' CS behavior? Our findings are as following. (1) The higher reputation has the manufacturer, the higher incentives for retailers at multi-channel are required in the equilibrium.
    shows the increasing pattern of optimal incentive level along the manufacturer's reputation level(a) under some parameter conditions(b=1/2;c=0;$\beta$=1/2). (2) The bigger complimentary externalities exists between the retailers' CS efforts, the higher incentives are required in the equilibrium.
    shows the increasing pattern of optimal incentive level along the complimentary externalities level($\beta$) under some parameter conditions(a=1;b=1/2;c=0). (3) The higher is the retailers' cost, the lower incentives are required in the equilibrium.
    shows the decreasing pattern of optimal incentive level along the cost level(c) under some parameter conditions(a=1;b=1/2;$\beta$=1/2). (4) The more competitive gets those two retailers, the higher incentives for retailers at multi-channel are required in the equilibrium.
    shows the increasing pattern of optimal incentive level along the competition level(b) under some parameter conditions(c=0;a=1;$\beta$=1/2). One of the major contribution points of this study is the fact that this study is the first to investigate the optimal CS incentive system under multi-channel setting.

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A Study on Consumer Characteristics According to Social Media Use Clusters When Purchasing Agri-food Online (온라인 농식품 구매시 소셜미디어 이용 군집에 따른 소비자특성에 대한 연구)

  • Lee, Myoung-Kwan;Park, Sang-Hyeok;Kim, Yeon-Jong
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.16 no.4
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    • pp.195-209
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    • 2021
  • According to the 2019-2020 social media usage survey conducted by the Seoul e-commerce center, 5 out of 10 consumers have experienced shopping through social media. The cost of traditional advertising media has been reduced and advertising spending on social media has risen by 74%, indicating that social media is becoming a more important marketing element. While the number of users of social media has increased and corporate marketing activities have increased accordingly, research has been conducted in various aspects of marketing such as user motivation for social media, satisfaction, and purchase intention. There was no subdivided study on the differences in the social media usage frequency of consumers in actual purchasing behavior. This study attempted to identify differences in consumer characteristics by cluster in the agrifood purchase situation by grouping them by type according to the frequency of use of social media for consumers who purchase agri-food online. Product involvement, product need, and online purchase channel Consumer characteristics such as demographic distribution, perceived risk, and eating and lifestyle in each cluster were checked for the three agrifood purchase situations including choice, and types for each cluster were presented. To this end, questionnaire data on the frequency of social media use and online agrifood purchase behavior were collected from 245 consumers, and the validity of the measurement variables was secured through factor analysis and reliability analysis. As a result of cluster analysis according to the frequency of social media use, it was divided into three clusters. The first cluster was a group that mainly used open social media, and the second cluster was a group that used both open and closed social media and online shopping malls; The third cluster was a group with low online media usage overall, and the characteristics of each cluster appeared. Through regression analysis, the effect on product involvement, product need, and purchase channel selection when purchasing agri-food online through each of the three clusters was confirmed through regression analysis. As a result of the regression analysis, the characteristic of cluster 1 in the situation of purchasing agri-food online is a male in his 30s living in a rural area who has no reluctance to purchase agri-food on social media or online shopping malls. The characteristics of cluster 2 are mainly consumers who are interested in purchasing health food, and the consumer characteristics are represented. In the case of cluster 3, when purchasing products online, they purchase after considering quality and price a lot, and the consumer characteristics are represented as people who are more confident in purchasing offline than online. Through this study, it is judged that by identifying the differences in consumer characteristics that appear in the agri-food purchase situation according to the frequency of social media use, it can be helpful in strategic judgments in marketing practice on social media customer targeting and customer segmentation.

Sentiment Analysis of Korean Reviews Using CNN: Focusing on Morpheme Embedding (CNN을 적용한 한국어 상품평 감성분석: 형태소 임베딩을 중심으로)

  • Park, Hyun-jung;Song, Min-chae;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.24 no.2
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    • pp.59-83
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    • 2018
  • With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

A Study for Improvement of Nursing Service Administration (병원 간호행정 개선을 위한 연구)

  • 박정호
    • Journal of Korean Academy of Nursing
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    • v.3 no.1
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    • pp.13-40
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    • 1972
  • Much has teed changed in the field of hospital administration in the It wake of the rapid development of sciences, techniques ana systematic hospital management. However, we still have a long way to go in organization, in the quality of hospital employees and hospital equipment and facilities, and in financial support in order to achieve proper hospital management. The above factors greatly effect the ability of hospitals to fulfill their obligation in patient care and nursing services. The purpose of this study is to determine the optimal methods of standardization and quality nursing so as to improve present nursing services through investigations and analyses of various problems concerning nursing administration. This study has been undertaken during the six month period from October 1971 to March 1972. The 41 comprehensive hospitals have been selected iron amongst the 139 in the whole country. These have been categorized according-to the specific purposes of their establishment, such as 7 university hospitals, 18 national or public hospitals, 12 religious hospitals and 4 enterprise ones. The following conclusions have been acquired thus far from information obtained through interviews with nursing directors who are in charge of the nursing administration in each hospital, and further investigations concerning the purposes of establishment, the organization, personnel arrangements, working conditions, practices of service, and budgets of the nursing service department. 1. The nursing administration along with its activities in this country has been uncritical1y adopted from that of the developed countries. It is necessary for us to re-establish a new medical and nursing system which is adequate for our social environments through continuous study and research. 2. The survey shows that the 7 university hospitals were chiefly concerned with education, medical care and research; the 18 national or public hospitals with medical care, public health and charity work; the 2 religious hospitals with medical care, charity and missionary works; and the 4 enterprise hospitals with public health, medical care and charity works. In general, the main purposes of the hospitals were those of charity organizations in the pursuit of medical care, education and public benefits. 3. The survey shows that in general hospital facilities rate 64 per cent and medical care 60 per-cent against a 100 per cent optimum basis in accordance with the medical treatment law and approved criteria for training hospitals. In these respects, university hospitals have achieved the highest standards, followed by religious ones, enterprise ones, and national or public ones in that order. 4. The ages of nursing directors range from 30 to 50. The level of education achieved by most of the directors is that of graduation from a nursing technical high school and a three year nursing junior college; a very few have graduated from college or have taken graduate courses. 5. As for the career tenure of nurses in the hospitals: one-third of the nurses, or 38 per cent, have worked less than one year; those in the category of one year to two represent 24 pet cent. This means that a total of 62 per cent of the career nurses have been practicing their profession for less than two years. Career nurses with over 5 years experience number only 16 per cent: therefore the efficiency of nursing services has been rated very low. 6. As for the standard of education of the nurses: 62 per cent of them have taken a three year course of nursing in junior colleges, and 22 per cent in nursing technical high schools. College graduate nurses come up to only 15 per cent; and those with graduate course only 0.4 per cent. This indicates that most of the nurses are front nursing technical high schools and three year nursing junior colleges. Accordingly, it is advisable that nursing services be divided according to their functions, such as professional, technical nurses and nurse's aides. 7. The survey also shows that the purpose of nursing service administration in the hospitals has been regulated in writing in 74 per cent of the hospitals and not regulated in writing in 26 per cent of the hospitals. The general purposes of nursing are as follows: patient care, assistance in medical care and education. The main purpose of these nursing services is to establish proper operational and personnel management which focus on in-service education. 8. The nursing service departments belong to the medical departments in almost 60 per cent of the hospitals. Even though the nursing service department is formally separated, about 24 per cent of the hospitals regard it as a functional unit in the medical department. Only 5 per cent of the hospitals keep the department as a separate one. To the contrary, approximately 12 per cent of the hospitals have not established a nursing service department at all but surbodinate it to the other department. In this respect, it is required that a new hospital organization be made to acknowledge the independent function of the nursing department. In 76 per cent of the hospitals they have advisory committees under the nursing department, such as a dormitory self·regulating committee, an in-service education committee and a nursing procedure and policy committee. 9. Personnel arrangement and working conditions of nurses 1) The ratio of nurses to patients is as follows: In university hospitals, 1 to 2.9 for hospitalized patients and 1 to 4.0 for out-patients; in religious hospitals, 1 to 2.3 for hospitalized patients and 1 to 5.4 for out-patients. Grouped together this indicates that one nurse covers 2.2 hospitalized patients and 4.3 out-patients on a daily basis. The current medical treatment law stipulates that one nurse should care for 2.5 hospitalized patients or 30.0 out-patients. Therefore the statistics indicate that nursing services are being peformed with an insufficient number of nurses to cover out-patients. The current law concerns the minimum number of nurses and disregards the required number of nurses for operation rooms, recovery rooms, delivery rooms, new-born baby rooms, central supply rooms and emergency rooms. Accordingly, tile medical treatment law has been requested to be amended. 2) The ratio of doctors to nurses: In university hospitals, the ratio is 1 to 1.1; in national of public hospitals, 1 to 0.8; in religious hospitals 1 to 0.5; and in private hospitals 1 to 0.7. The average ratio is 1 to 0.8; generally the ideal ratio is 3 to 1. Since the number of doctors working in hospitals has been recently increasing, the nursing services have consequently teen overloaded, sacrificing the services to the patients. 3) The ratio of nurses to clerical staff is 1 to 0.4. However, the ideal ratio is 5 to 1, that is, 1 to 0.2. This means that clerical personnel far outnumber the nursing staff. 4) The ratio of nurses to nurse's-aides; The average 2.5 to 1 indicates that most of the nursing service are delegated to nurse's-aides owing to the shortage of registered nurses. This is the main cause of the deterioration in the quality of nursing services. It is a real problem in the guest for better nursing services that certain hospitals employ a disproportionate number of nurse's-aides in order to meet financial requirements. 5) As for the working conditions, most of hospitals employ a three-shift day with 8 hours of duty each. However, certain hospitals still use two shifts a day. 6) As for the working environment, most of the hospitals lack welfare and hygienic facilities. 7) The salary basis is the highest in the private university hospitals, with enterprise hospitals next and religious hospitals and national or public ones lowest. 8) Method of employment is made through paper screening, and further that the appointment of nurses is conditional upon the favorable opinion of the nursing directors. 9) The unemployment ratio for one year in 1971 averaged 29 per cent. The reasons for unemployment indicate that the highest is because of marriage up to 40 per cent, and next is because of overseas employment. This high unemployment ratio further causes the deterioration of efficiency in nursing services and supplementary activities. The hospital authorities concerned should take this matter into a jeep consideration in order to reduce unemployment. 10) The importance of in-service education is well recognized and established. 1% has been noted that on the-job nurses. training has been most active, with nursing directors taking charge of the orientation programs of newly employed nurses. However, it is most necessary that a comprehensive study be made of instructors, contents and methods of education with a separate section for in-service education. 10. Nursing services'activities 1) Division of services and job descriptions are urgently required. 81 per rent of the hospitals keep written regulations of services in accordance with nursing service manuals. 19 per cent of the hospitals do not keep written regulations. Most of hospitals delegate to the nursing directors or certain supervisors the power of stipulating service regulations. In 21 per cent of the total hospitals they have policy committees, standardization committees and advisory committees to proceed with the stipulation of regulations. 2) Approximately 81 per cent of the hospitals have service channels in which directors, supervisors, head nurses and staff nurses perform their appropriate services according to the service plans and make up the service reports. In approximately 19 per cent of the hospitals the staff perform their nursing services without utilizing the above channels. 3) In the performance of nursing services, a ward manual is considered the most important one to be utilized in about 32 percent of hospitals. 25 per cent of hospitals indicate they use a kardex; 17 per cent use ward-rounding, and others take advantage of work sheets or coordination with other departments through conferences. 4) In about 78 per cent of hospitals they have records which indicate the status of personnel, and in 22 per cent they have not. 5) It has been advised that morale among nurses may be increased, ensuring more efficient services, by their being able to exchange opinions and views with each other. 6) The satisfactory performance of nursing services rely on the following factors to the degree indicated: approximately 32 per cent to the systematic nursing activities and services; 27 per cent to the head nurses ability for nursing diagnosis; 22 per cent to an effective supervisory system; 16 per cent to the hospital facilities and proper supply, and 3 per cent to effective in·service education. This means that nurses, supervisors, head nurses and directors play the most important roles in the performance of nursing services. 11. About 87 per cent of the hospitals do not have separate budgets for their nursing departments, and only 13 per cent of the hospitals have separate budgets. It is recommended that the planning and execution of the nursing administration be delegated to the pertinent administrators in order to bring about improved proved performances and activities in nursing services.

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