Purpose: The purpose of this study was to investigate the educational needs and knowledge level of traditional Korean nursing among nurses in Korean medicine hospitals. Methods: A survey design was used. A total of 180 nurses working for more than six months at 10 Korean medicine participated in this study. Data were collected in September of 2019. All data were analyzed by t-test, ANOVA, Scheffé, and paired t-test using SPSS Statistics 25.0 program. Results: The six sub-areas of educational needs for traditional Korean nursing were knowledge of treatments, direct nursing care, types of acupuncture, manipulative therapy, diagnosis, and herbal medicine in order. Average score of the educational needs for nurses in Korean medicine hospitals was 3.77 points out of 5 points. All six sub-areas of the knowledge level were statistically significant. Average score of knowledge level about Korean medicine among nurses was 3.03 out of 5. Conclusion: As a result of this study, it was found that a high level of knowledge is required or Korean medicine education. Knowledge of Korean medicine should be improved through education on thetypes of acupuncture, manipulative therapy, diagnosis, and treatment with relatively low scores. The results of this study can be used as basic data for preparing an educational system to improve the knowledge level of nurses in Korean medicine hospitals.
Most countries recognize traditional knowledge as an economical resource in recent years, and so are actively participating in WIPO discussions for making sure of their intellectual property rights. In this study, the definition of traditional knowledge was discussed for making clear its categories and relative subjects. A tool for Korean Traditional Knowledge Resource Classification(KTKRC) was developed for putting the data of the resources in order, and was indispensable for searching for and examining cultural artifacts within the system of international intellectual property rights. KTKRC covers comprehensively our various traditional knowledge resources and has a similar structure to IPC for international searching, examining, and information exchange. KTKRC consists of a section of traditional knowledge(A), and three subsections: production technology(A0), living technology(A2) and creative technology(A4). The subsections include 8 classes, 28 subclasses, 105 groups, and a great number of subgroups.
Aleksey L. Kim;Hyeon Jin Jeong;Ju Eun Jang;Hyeok Jae Choi;Chang-Gee Jang;Hee-Young Gil
한국자원식물학회:학술대회논문집
/
한국자원식물학회 2022년도 추계학술대회
/
pp.48-48
/
2022
Ethnobotany is an interdisciplinary science at the intersection of botany and ethnology. Currently, there is a sharply increasing need for the study and conservation of traditional knowledge about plants. The loss of traditional sources, knowledge, and practices in using plants is caused by the growth of technologies in all branches of production, widespread urbanization, and globalization of the economy. This study was been conducted to collect and analyze the Koryoins (Koryo saram) traditional ethnobotanical knowledge, living in Uzbekistan, whose number 174,200 people. They are the descendants of Korean immigrants to the Russian Far East, who ended up in Central Asia as a result of the forced resettlement in 1937. In the processing of collected data, four main categories of uses were defined - Alimentary, Medicinal, Household/Handicraft, and Others. For quantitative data analysis, synthetic indices were used - RFC (Relative Frequency of Citation) and CI (Cultural Importance Index), which are commonly applied to assess the importance of plants. The respondents mentioned 72 plants belonging to 28 botanical families. A significant part of them was cultivar plants. The category that had the largest number of plants mentioned by the respondents was the Alimentary use category (51). According to quantitative indices rates, the most important plants are traditionally used for food. A comparison of ethnobotanical knowledge was made with the collected data of this study and Korean traditional knowledge.
Ever since the dawn of civilization, the ambient vegetation and the resources constituted major source of human existence for various substantial requirements. Our present knowledge on plant resources emerged from the traditional heritable knowledge descended from generation to generation. However, traditional knowledge pertaining to several aspects remained untapped from various remote localities or populations. Furthermore, with the present trends of excessive exploitation of natural resources and degradation of habitats, conservation and ecological management require coherence of traditional skills and modern approaches. Therefore, the present study is to record traditional plant based knowledge among the inhabitants of Siwalik region of Uttarakhand Himalaya. Extensive field survey was made for the collection of data on the medicinal aspects of plant species in the study area covering the parts of districts Pauri, Dehradun and Haridwar. During the course of study 130 plant species belonging to 65 families are reported, used as traditional medicine by the local inhabitants of this region.
The main purpose of this research was to study and analyze the actual utilization of traditional knowledge resources and to search for methods to activate local communities through utilization of traditional knowledge resources best suited for us. For this study, data listed on the internal web sites during August 2002 to October 2002 were searched and analyzed. In terms of statistical analysis, frequency, percentage, and x$^2$-test were operated using the SPSS 10.0 program. The major results of this study are as follows: 1) Traditional knowledge resources utilized throughout the nation totaled to 8,906 cases. These utilized resources composed of 48.0% of tangible resources, 32.3% of environmental resources, and 19.8% of intangible resources and such utilized resources were in order of life-skill, scenery, ruins and relics, community activity, exhibition, and folklores. 2) Tourism, merchandising, and festival were the major types of utilization of traditional knowledge resources, while education was the relatively minor portion in utilization type. 3) Compound linking of traditional knowledge resources, utilization type, and utilizing body showed links such as life skill-merchandising-civilian, ruins and relics-tourism-government, folklore-festival-civilian, scenery-tourism-government, and exhibition-education-civilian.
Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.
Research methodology on Traditional Medicine in East Asia refers to logical thinking system, empirical positivism system and methodology of developing these knowledge systems. Logical thinking system of abstract concepts such as analogy or abduction and positivism system of reasonable explanation such as the five elements and their characteristic theory have been used in various ways empirically or in the form of humanities and knowledge system was developed through parallel structure of empirical positivism and exegetical studies. After the 16th century, evidence was required along with the tradition of putting emphasis on rationality, logicality and empirical positivism and characteristics of medical humanities can be found in emphasizing on medical ethics. Data that can be considered as structural review paper or meta analysis from original data of research on Traditional East Asian Medicine should be evaluated as historical evidence which is equivalent to specialist opinion, descriptive disease research, single case report or case series. Historical evidence based medicine is a research method using Historical evidence to selectively support data that are faithful to traditional theory with higher possibility to be used in future traditional east Asian medicine that links between traditional knowledge and scientific research methodology. Moreover, historical evidence based medicine tries to re-evaluate the value of traditional knowledge and ultimately, guides the direction of development of traditional medicine through scientific rationality based on history and culture.
Objectives : This study aims to record and conserve oral traditional knowledge of medicinal animals from the indigenous people living in the local communities of Songnisan National Park, Korea. Methods : Data was collected by participatory observations and in-depth interviews with semi-structured questionnaires. Quantitative comparative analyses were accomplished through data received from the following three methods: informant consensus factor (ICF), fidelity level (FL), and network analysis. Results : The investigation reveals that the indigenous people have used 49 species of medicinal animals distributed within 45 genera, belonging to 39 families with 336 different usages. According to the distribution of recorded families, the most representative families were Scolpendridae and Phasianidae, which were utilized 36 times each (10.71 % each). The category with the highest degree of consensus from informants was disorders related to the nervous system (0.97). 16 species were classified with a fidelity level of 100 %. The network analysis revealed that a lack of vigor was related to 23 species, including Agkistrodon blomhoffii, Gallus domesticus, and Canis familiaris, among the total 49 species investigated. Conclusions : This documentation can help preserve the traditional knowledge and local health traditions of Korea that are disappearing due to rapid industrialization, urbanization, and death of the elderly with traditional knowledge. Additionally, the animals investigated in this study can be developed into medicinal food and drug for treating specific health conditions through further research.
In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.
Background: This study aimed to determine nursing student knowledge, behavior and beliefs for breast cancer and breast self-examination receiving courses with a traditional lecturing method (TLM) and the Six Thinking Hats method (STHM). Materials and Methods: The population of the study included a total of 69 second year nursing students, 34 of whom received courses with traditional lecturing and 35 of whom received training with the STHM, an active learning approach. The data of the study were collected pre-training and 15 days and 3 months post-training. The data collection tools were a questionnaire form questioning socio-demographic features, and breast cancer and breast self-examination (BSE) knowledge and the Champion's Health Belief Model Scale. The tests used in data analysis were chi-square, independent samples t-test and paired t-test. Results: The mean knowledge score following traditional lecturing method increased from $9.32{\pm}1.82$ to $14.41{\pm}1.94$ (P<0.001) and it increased from $9.20{\pm}2.33$ to $14.73{\pm}2.91$ after training with the Six Thinking Hats Method (P<0.001). It was determined that there was a significant increase in pre and post-training perceptions of perceived confidence in both groups. There was a statistically significant difference between pre-training, and 15 days and 3 months post-training frequency of BSE in the students trained according to STHM (p<0.05). On the other hand, there was a statistically significant difference between pre-training and 3 months post-training frequency of BSE in the students trained according to TLM. Conclusions: In both training groups, the knowledge of breast cancer and BSE, and the perception of confidence increased similarly. In order to raise nursing student awareness in breast cancer, either of the traditional lecturing method or the Six Thinking Hats Method can be chosen according to the suitability of the teaching material and resources.
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