• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.11-27
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• 2024

This study aimed to investigate the role of scientific empathy in influencing students' productive disciplinary engagement in scientific activities and analyze the key factors of scientific empathy that manifest during this process. Twelve fifth-grade students were divided into three subgroups based on their general empathic abilities. Lessons promoting productive disciplinary engagement, integrating design thinking processes, were conducted. Subgroup discourse analysis during idea generation and prototype stages, two of five problem-solving steps, enabled observation of scientific empathy and practice aspects. The results showed that applying scientific empathy effectively through design thinking facilitated students' productive disciplinary engagement in science. In the idea generation stage, we observed an initial increase followed by a decrease in scientific empathy and practice utterances, while during the prototyping stage, utterance frequency increased, particularly in the later part. However, subgroups with lower empathic abilities displayed decreased discourse frequency in scientific empathy and practice during the prototype stage due to a lack of collaborative communication. Across all empathic ability levels, the students articulated all five key factors of scientific empathy through their utterances in situations involving productive science engagement. In the high empathic ability subgroup, empathic understanding and concern were emphasized, whereas in the low empathic ability subgroup, sensitivity, scientific imagination, and situational interest, factors of empathizing with the research object, were prominent. These results indicate that experiences of scientific empathy with research objects, beyond general empathetic abilities, serve as a distinct and crucial factor in stimulating diverse participation and sustaining students' productive engagement in scientific activities during science classes. By suggesting the potential multidimensional impact of scientific empathy on productive disciplinary engagement, this study contributes to discussions on the theoretical structure and stability of scientific empathy in science education.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• (The) Research of the performance art and culture
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• no.25
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• pp.253-289
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• 2012

Dasiraegi is a traditional funeral rite performance of Jindo located in the South Jeolla Province of South Korea. With its unique stylistic structure including various dances, songs and witty dialogues, and a storyline depicting the birth of a new life in the wake of death, embodying the Buddhism belief that life and death is interconnected; it attracted great interest from performance organizers and performers who were desperately seeking new contents that can be put on stage as a performance. It is needless to say previous research on Dasiraegi had been most valuable in its recreation as it analyzed the performance from a wide range of perspectives. Despite its contributions, the previous researches were mainly academic focusing on: the symbolic meanings of the performance, basic introduction to the components of the performance such as script, lyrics, witty dialogue, appearance (costume and make-up), stage properties, rhythm, dance and etc., lacking accurate representation of the most crucial element of the performance which is sori (song). For this reason, the study analyzes the music of Dasiraegi and presents its musical characteristics along with its scores to provide practical support for performers who are active in the field. Out of all the numbers in Dasiraegi, this study analyzed all of Geosa-nori and Sadang-nori, the funeral dirge (mourning chant) sung as the performers come on stage and Gasangjae-nori, because among the five proceedings of the funeral rite they were the most commonly performed. There are a plethora of performance recordings to choose from, however, this study chose Jindo Dasiraegi, an album released by E&E Media. The album offers high quality recordings of performances, but more importantly, it is easy to obtain and utilize for performers who want to learn the Dasiraegi based on the script provided in this study. The musical analysis discovered a number of interesting findings. Firstly, most of the songs in Dasiraegi use a typical Yukjabaegi-tori which applies the Mi scale frequently containing cut-off (breaking) sounds. Although, Southern Kyoung-tori which applies the Sol scale was used, it was only in limited parts and was musically incomplete. Secondly, there was no musical affinity between Ssitgim-gut and Dasiraegi albeit both are for funeral rites. The fundamental difference in character and function of Ssitgim-gut and Dasiraegi may be the reason behind this lack of affinity, as Ssitgim-gut is sung to guide the deceased to heaven by comforting him/her, whereas, Dasiaregi is sung to reinvigorate the lives of the living. Lastly, traces of musical grammar found in Pansori are present in the earlier part of Dasiraegi. This may be attributed to the master artist (Designee of Important Intangible Cultural Heritage), who was instrumental in the restoration and hand-down of Dasiaregi, and his experience in a Changgeuk company. The performer's experience with Changgeuk may have induced the alterations in Dasiraegi, causing it to deviate from its original form. On the other hand, it expanded the performative bais by enhancing the performance aspect of Dasiraegi allowing it to be utilized as contents for Performing Arts. It would be meaningful to see this study utilized to benefit future performance artists, taking Dasiraegi as their inspiration, which overcomes the loss of death and invigorates the vibrancy of life.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• The Korean Nurse
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• v.20 no.4 s.112
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• pp.27-38
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• 1981

오늘 저희에게 주어진 주제, 내일에 타당한 간호사업 및 간호교육의 향방을 어떻게 정하여야 하는가의 논의는 오늘날 간호계 주변에 일어나고 있는 변화의 실상을 이해하는 데서 비롯되어져야 한다고 생각하는 입장에서 먼저 세계적으로 건강관리사업이 당면한 딜레마가 어떠한 것이며 이러한 문제해결을 위해 어떠한 새로운 제안들이 나오고 있는가를 개관 하므로서 그 교육적 의미를 정의해 보고 장래 간호교육이 지향해야할 바를 생각해 보려 합니다. 오늘의 사회의 하나의 특징은 세계 모든 나라들이 각기 어떻게 전체 국민에게 고루 미칠 수 있는 건강관리체계를 이룩할 수 있느냐에 관심을 모으고 있는 사실이라고 봅니다. 부강한 나라에 있어서나 가장 빈궁한 나라에 있어서나 그 관심은 마찬가지로 나타나고 있읍니다. 보건진료 문제의 제기는 발달된 현대의학의 지식과 기술이 지닌 건강관리의 방대한 가능성과 건강 관리의 요구를 지닌 사람들에게 미치는 실질적인 혜택간에 점점 더 크게 벌어지는 격차에서 발생한다고 봅니다. David Rogers는 1960년대 초반까지 갖고 있던 의료지식의 축적과 민간인의 구매력 향상이 자동적으로 국민 건강의 향상을 초래할 것이라고 믿었던 순진한 꿈은 이루어지지 않았고 오히려 의료사업의 위기는 의료지식과 의료봉사간에 벌어지는 격차와 의료에 대한 막대한 투자와 그에서 얻는 건강의 혜택간의 격차에서 온다고 말하고 있읍니다. 균등 분배의 견지에서 보면 의료지식과 기술의 향상은 그 단위 투자에 대한 생산성을 낮춤으로서 오히려 장애적 요인으로 작용해온 것도 사실이고 의료의 발달에 따른 일반인의 기대 상승과 더불어 의료를 태성의 권리로 규명하는 의료보호사업의 확대로 야기되는 의료수요의 급증은 모두 기존 시설 자원에 압박을 초래하여 전래적 의료공급체제에 도전을 가해 왔으며 의료의 발달에 건 기대와는 달리 인류의 건강 문제 해결은 더욱 요원한 과제로 남게 되었읍니다. 현시점에서 세계인구의 건강문제는 기아, 영양실조, 안전한 식수 공급 및 위생적 생활환경조성의 문제에서부터 가장 정밀한 의료기술발달에 수반되는 의료사회문제에 이르는 다양한 문제를 지니고 있으며 주로 각개 국가의 경제 사회적 여건이 이 문제의 성격을 결정짓고 있다고 볼수 있읍니다. 그러나 건강 관리에 대한 요구는 영구히, 완전히 충족될 수 없는 요구에 속한다는 의미에서 경제 사회적 발달 수준에 상관없이 모든 국가가 공히 요구에 미치지 못하는 제한된 자원문제로 고심하고 있는 실정입니다. 또 하나의 공통된 관점은 각기 문제의 상황은 달라도 오늘날의 건강 문제는 주로 의료권 밖의 유전적 소인, 사회경제적, 정치문화적인 환경여건과 각기 선택하는 삶의 스타일에 깊이 관련되어 있다는 사실입니다. 따라서 오늘과 내일의 건강관리 문제는 의학적 견지에서 뿐 아니라 널리 경제, 사회, 정치, 문화적 관점에서 포괄적인 접근이 시도되어야 한다는 점과 의료의 고급화, 전문화, 일변도의 과정에서 소외되었던 기본건강관리체계 강화에 역점을 둔 다양하고 탄력성 있는 사업전개가 요구되고 있다는 점입니다. 다양한 건강관리요구에 적절히 대처할 수 있기 위한 그간 세계 각처에서 시도된 새로운 건강관리 접근과 그 제안을 살펴보면 대체로 4가지의 뚜렷한 성격들로 집약할 수 있을 것 같습니다. 그 첫째는 건강관리사업계획 및 그 수행에 있어 지역 사회의 적극적 참여를 유도하는 일, 둘째는 지역단위의 일차보건의료에서 부터 도심지 신예 종합병원, 시설 의료에 이르기까지 건강관리사업을 합리적으로 체계화하는 일. 셋째로 의료인력이용의 효율화 및 비의료인의 훈련과 협조 유발을 포함하는 효과적인 인력관리에 대한 제안과 넷째로 의료보험 및 각양 집단 의료유형을 포함하는 대체 의료재정 운영관리에 관련된 제안들을 들 수 있읍니다. 건강관리사업에 있어 지역사회 참여의 의의는 첫째로 사회 경제적인 제약이 모든 사람에게 가능한 최대한의 의료를 모두 고루 공급하기 어렵게 하고 있다는 점에서 제한된 정부재정과 지역사회가용자원을 보다 효율적으로 이용할 수 있게 하는 자조적이고 자율적인 지역사회건강관리체제의 구현에 있다고 볼 수 있으며 둘때로는 개인과 가족 및 지역민의 건강에 영향하는 많은 요인들은 실질적으로 의료권 외적 요인들로서 위생적인 생활양식, 식사습관, 의료시설이용 등 깊이 지역사회특성과 관련되어 국민보건의 실질적 향상을 위하여는 지역 주민의 자발적인 참여가 필수여건이 된다는 점 입니다. 지역 단위별 체계적인 의료사업의 전개는 제한된 의료자원의 보다 합리적이고 효율적인 이용을 가능하게 하며 요구가 있을때 언제나 가까운 거리에서 경제 사회적 제약을 받지 않고 이용할 수 있는 일차건강관리망을 통하여 건강에 관련된 정보를 얻으며 질병예방, 건강증진 및 기초적인 진료의 도움을 얻을 수 있고 의뢰에 대한 제2차, 제3차 진료에의 길은 건강관리사업의 질과 폭을 동시에 높고 넓게 해 줄 수 있는 길이 된다는 것입니다. 인력 관리에 관련된 두가지 기본 방향으로서는 첫째로 기존보건의료인력의 적정배치 유도이고 둘째는 기존인력의 역할확대, 조정 및 비의료인의 교육훈련과 부분적 업무대체를 들수 있으며 이러한 인력관리의 기본 방향은 부족되는 의료인력의 생산성을 높이고 주민들의 자조적 능력을 강화시킨다는 데에 두고 있음니다. 대체적 의료재정운영안은 대체로 의료공급과 재정관리를 이원화하여 주민의 경제능력이 의료수혜의 장애요소로 작용함을 막고 의료인의 경제적 동기에 의한 과잉치료처치에 의한 낭비를 줄임으로써 의료재정의 투자의 효과를 증대하는 데(cost-effectiveness) 그 기본방향을 두고 있다고 봅니다. 이러한 주변의료 사회적인 동향이 간호교육의 미래상에 끼치는 영향은 지대한 것이라 봅니다. 첫째로 장래 세계인구의 건강문제는 정치, 사회, 경제, 환경적인 의료권 밖의 요인들에 의해 더욱 크게 영향 받는다고 전제한다면 건강문제해결에 있어서도 전통적인 의료사업의 접근에서 더나아가 문제발생의 근원이 되는 생활개선이라는 차원에서 포괄적 접근을 생각하여야 하고 이를 위해선 정치, 경제, 사회전반에 걸친 깊이있는 이해과 주민의 생활환경에 직접 영향하는 교통수단, 통신망 mass media, 전력문제, 농업경영방법 및 조직적 사회활동 등 폭넓은 이해가 요구된다고 봅니다. 둘째로, 지역사회참여의 의의를 인정한다면 지역민의 자발적 참여를 효과적으로 유발시킬수 있고 의료집단과 각종 주민조직과 일반주민들 사이에서 협조적으로 일할수 있는 역량을 기르기위한 교육적 준비가 요구된다고 봅니다. 셋째로, 지역주민의 건강관리 자조능력 강화를 하나의 목표로 삼는다면 치료자에서 교육자로, 지도자에서 촉진자로, 제공자에서 지원자료의 역할의 변화 내지 다양화를 요구하게 될 것이므로 그에 대처할 수 있는 준비가 필요하다고 봅니다. 넷째로, 생각되어야 할 점은 지역중심건강관리사업을 지향하는 보건의료의 이념적 방향과 그에 상응하는 구체적 접근방법을 효율적으로 적용하기 위해서는 종횡으로 연결되는 의사소통체계의 정립과 민활한 정보교환이 이루어질 수 있어야 한다는 점에서 의사소통의 구심체로서 역할할 수 있는 역량을 함양해야 할 교육적 과제가 있다고 봅니다. 마지막으로 생각되어야 할 점은 지역중심으로 전개될 건강관리사업은 건강증진 및 질병예방적 측면과 질병진료 및 회복과 재활에 이르는 종합적이고 포괄적인 사업이어야 한다는 점에서 종래 공공 의료부문과 사설의료기관 사이에 나누어져 있던 예방의학과 치료의학의 통합 뿐 아니라 정부주축으로 이루어 지고 있는 지역사회개발사업 및 농촌지도사업과 종교 및 각종 민간인 집단이 벌이고있는 사업들과의 전체적인 통합적 접근이 이루어져야 한다고 생각하는 입장에서 종래 간호교육이 강조하지 않던 진료의 의무와 대외적 조직활동에 대한 보완적인 교육조치가 요구된다고 봅니다. 간호의 학문체계로서의 입장은 오랜 역사를 두고 논의의 대상이 되어왔으나 아직까지 뚜렷이 어떤 것이 간호 특유의 지식체계이며 건강문제에 관련하여 무엇이 간호특유의 결정영역이며 이 결정과 그 결과를 어떠한 방법으로 치료적 행위로 옮길 수 있는가에 대한 확실한 답을 얻지 못하고 있는 실정이라고 봅니다. 다만 근래에 제시된 여러 간호이론들 속에서 공통적으로 이야기되어지고 있는 개념들로선 우선 간호학문을 건강과 질병에 관련된 인간의 전인적이고 전체적인 상황을 다루는 학제적 과학으로서보는 입장이 있고 따라서 생물신체적인 면 외에 정신심리적, 사회경제적, 정치문화적 환경과의 상호작용 속에서 인간의 건강과 질병문제를 생각한다는 지향을 갖고 있다고 말할 수 있겠읍니다. 간호교육은 간호계 내적인 학문적, 이론적 체계화의 요구에 못지않게 대민봉사하는 전문직으로서의 사회적 책임을 감당해야하는 중요과제를 안고있어 변화하는 사회요구에 효과적으로 대처해 나가야 할 당면문제를 안고 있읍니다. 간효역할 확대, 보건진료원훈련 등 이러한 사회적 요구에 대응하려는 조치가 되겠읍니다. 이러한 시점에서 간호계가 분명히 짚고 넘어가야 할 사실은 이러한 움직임들이 종래의 의사들의 외업무공급을 연장 확대하는 입장에 서서 간호의 특수전문직 명목을 흐리게 할수있는 위험을 감수할 것인지 아니면 가능한 대체방안을 갖고 간호전문직의 독자적인 진로를 개척하면서 다각적인 도전을 받아들일 준비를 갖추든지 그 방향을 뚜렷이 해야할 일이라 생각합니다. 저로서는 이미 잘 훈련된 간호원들과 조산원들의 교육적, 경험적 배경을 기반으로 지역사회 최일선 건강관리요원으로 사회적 효능을 다 할수 있는 일차건강관리간호조직의 구현을 대체방안으로 제시하고 싶습니다. 간호원과 조산원들의 훈련된 역량과 건강관리체제의 구조적 변화를 효과적으로 조화시킨다면 대부분의 세계인구의 건강문제는 해결가능하다고 보는 입장입니다. 물론 정책과 의료와 행정적지원이 활성화되어지는 환경속에서만 그 기대하는 결과가 확대되리라는 점 부언하는 바입니다. 마지막으로 언급하고 싶은 점은 바로 오늘의 주제 ''교육의 동역자-선생과 학생''이라는 개념입니다. 특히 상회정의적 입장에서 보는 의료사업전개에 지역민 내지 의료소비자의 참여를 강조하는 현시점에 있어 교육자와 학생이 교육의 현장에서 서로 동역자로서 학습의 책임을 나누는 경험은 아주 시기적으로 적합하여 교육적으로 지대한 의미를 갖는 것이라고 생각합니다. 이에 수반되어져야 할 역할의 변화에 수용적인 자세를 갖고 적극 실제적용하려 노력하는 선생앞에서 자주적 결정을 행사해본 학생이야말로 건강관리대상자로 하여금 같은 결정권을 행사할수 있도록 촉구하여 주민의 자조적 역량을 기르고 의료사업의 민주화, 인간화를 이룩할 수 있는 길잡이가 될 수 있으리라 믿는 바입니다.