• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.216-225
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• 2023

Background: Tuberculosis (TB)-related stigma has been well-documented. Since the emergence of the coronavirus disease 2019 (COVID-19), different organizations have been alerted to the fact that stigma could arise again. Due to stigma's negative effects, this qualitative study aimed to explore the stigma felt by patients by evaluating the following: COVID-19 stigma and its temporal progression through the pandemic; stigma perceived by different patients with TB before and during COVID-19 pandemic; and difference perceived by individuals who contracted both diseases. Methods: A semi-structured interview was developed according to the available literature on the theme. It was performed individually in 2022 upon receiving signed informed consent. Participants were recruited with a purposive sampling approach by searching medical records. Those who currently or previously had pulmonary TB and/or COVID-19 were included. Data were subjected to thematic analysis. Results: Nine patients were interviewed, including six (66.7%) females. The median age of patients was 51±14.7 years. Four participants (44.4%) had completed high school and four (44.4%) were never smokers. Three had both TB and COVID-19. Four only had TB and two only had COVID-19. Interviews identified eight main themes: knowledge and beliefs, with several misconceptions identified; attitudes towards the disease, varying from social support to exclusion; knowledge and education, assumed as of extreme importance; internalized stigma, with self-rejection; experienced stigma, with discrimination episodes; anticipated stigma, modifying actions for avoiding stigma; perceived stigma, with judgment by others prevailed; and temporal evolution of stigma. Conclusion: Individuals expressed strong stigma for both diseases. De-stigmatization of respiratory infectious diseases is crucial for limiting stigma's negative impact.

• Korean Journal of Culture and Social Issue
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• v.21 no.4
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• pp.719-738
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• 2015

The purpose of this study is to examine distress and difficulties experienced by multi-cultural elementary students in our society and to identify replaceable resources related to their school adjustment. For this, semi-structured interview consisting of questions related to these issues was conducted to 14 multi-cultural elementary students of 3^rd to 6^th grade who were living in a metropolitan area. Qualitative data were analyzed based on Giorgi's(1985) method of phenomenological analysis, which led to three dimensions in relation to elementary school adjustment: individual, relationship, and societal dimensions. Also, for school adjustment, education was found to be needed at the level of individuals, family, and multi-cultural cognition. Lastly, as for multi-cultural elementary students to adjust well not only at the school but also in Korean society overall, they must have positive national identity and multi-cultural recognition. Finally, their school adjustment were discussed in relation to these findings.

• Korean Journal of Culture and Social Issue
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• v.21 no.1
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• pp.21-43
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• 2015

The purpose of this study was to examine cultural differences and related difficulties which KoreanChinese women in Korean society experience at work and the different styles of their cultural adaptation. For this, semi-structured interviews consisting of questions related to these issues were conducted on 17 Korean-Chinese women living in the metropolitan area (average age: 34, SD = 9.25, average stay in Korea = 4 years, SD = 2.24). After analysis of qualitative data based on Giorgi's(1985) method of phenomenological analysis, a total of 225 significant statements were found and those were grouped into 23 subcategories, which were then grouped again into 9 categories. Cultural differences and related difficulties appeared in identity, verbal communication, political and economic aspects, and relational and sexual affairs. It was also revealed that Korean-Chinese women adapted in the three ways of Active, Passive, and Avoident. Lastly, various social actions that can aid the adaptation of Korean-Chinese women to Korea based on these results were discussed.

• Industry Promotion Research
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• v.8 no.4
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• pp.209-220
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• 2023

This study used general qualitative research and G-PhotoVoice methods to study the experience of psychiatric patients participating in vocational rehabilitation programs in daytime hospitals. The researcher in this study conducted interviews more than two times with five patients who took part in the program and data was also collected from additional conversations while patients were admitted or on outpatient visit after being discharged from the day hospital. In addition, semi-structured group interview with photos was also conducted. As a result of the study, the participants' experience of participating in the day hospital vocational rehabilitation program was found to be "experiencing difficulties in vocational activities," "joying working together," "getting a sense of stability," and "feeling that they have grown."Based on these findings, this study discussed the implications of activating vocational rehabilitation in daytime hospitals and promoting the growth of psychiatric patients.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.176-184
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• 2024

Objectives: The dengue prevention program known as "One House One Mosquito Larva Inspector" involves health volunteers who play a crucial role in the surveillance of mosquito larvae and reporting their findings to local public health officials. This study aimed to identify factors related to the knowledge, attitudes, and practices (KAP) of dengue prevention behavior among these health volunteers. Methods: A study was conducted in 5 sub-districts in Malang, an urban area in Indonesia. We employed a cross-sectional design and utilized a semi-structured questionnaire to assess the KAP of 400 health volunteers. Data were collected through face-to-face interviews. Results: Multiple logistic regression analysis revealed that individuals with a more positive attitude (odds ratio [OR], 1.69; p<0.05) and those with family sizes greater than five persons (OR, 1.90; p<0.05) were more likely to engage in effective dengue prevention practices. Additionally, possesing good knowledge was significantly assocated with more positive attitude (OR, 2.24; p<0.001). Furthermore, 40% reduction in positive attitude was observed in those over 45 years (OR, 0.60; p<0.05). The best practices most frequently reported by the majority of respondents included always reporting their surveillance activities (75.8%) and cleaning the water container in the bathroom at least once a week (65.2%). However, only 52.2% of respondents regularly checked for mosquito larvae in their neighborhood. Conclusions: Sustainable promotion and training for the "One House One Mosquito Larva Inspector" initiative are necessary, particularly among young health volunteers, to improve dengue prevention behaviors both within their own homes and in the surrounding environment.

• Journal of The Korean Association For Science Education
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• v.27 no.3
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• pp.253-262
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• 2007

This study is an investigation regarding the understanding of the nature of science among pre-service science teachers majoring in science education. We interviewed 22 senior students in science education who finished their internship courses. Students were interviewed individually for approximately 20 minutes each. Data from semi-structured interview were audio-recorded and transcribed for the analysis. Findings indicated that participants held more complete understanding of the nature of scientific knowledge than the nature of scientific enterprise. Their understandings of the nature of scientific method was that hypothetical-deductive method is more scientific than descriptive-narrative method and there is a single stepwise scientific method to solve problems. These results showed that they have a narrow view of the nature of science. Thus, teacher education programs need to integrate the understanding of the nature of science throughout.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.475-486
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• 2020

Climate change scenarios are the basis of research to cope with climate change, and consist of large-scale spatio-temporal data. From the data point of view, one scenario has a large capacity of about 83 gigabytes or more, and the data format is semi-structured, making it difficult to utilize the data through means such as search, extraction, archiving and analysis. In this study, a tool for analyzing extreme climate events based on spatial information is developed to improve the usability of large-scale, multi-period climate change scenarios. In addition, a pilot analysis is conducted on the time and space in which the heavy rain thresholds that occurred in the past can occur in the future, by applying the developed tool to the RCP8.5 climate change scenario. As a result, the days with a cumulative rainfall of more than 587.6 mm over three days would account for about 76 days in the 2080s, and localized heavy rains would occur. The developed analysis tool was designed to facilitate the entire process from the initial setting through to deriving analysis results on a single platform, and enabled the results of the analysis to be implemented in various formats without using specific commercial software: web document format (HTML), image (PNG), climate change scenario (ESR), statistics (XLS). Therefore, the utilization of this analysis tool is considered to be useful for determining future prospects for climate change or vulnerability assessment, etc., and it is expected to be used to develop an analysis tool for climate change scenarios based on climate change reports to be presented in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.11-20
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• 2010

XML view is proposed to integrate between XML data and heterogeneous data over distributed environment and global XML view is used to search distributed heterogeneous data. At this time, standard query language for user is XQuery and the method for processing global XQuery queries over distributed environment is one of the new research topics. One of the basic and simple methods to process distributed SQL queries is that generates local queries for processing a global query and constructs the result of the global query from the results of the local queries. However, the syntax of XQuery differs from SQL because the XQuery contains some special expressions like FOR clauses for querying to semi-structured data, of course, FOR clauses are not used in SQL. Therefore, there are some problems to adopt the method for processing global SQL queries for generating local XQuery queries. This paper defines some problems when generates local XQuery queries for processing global XQuery queries and proposes a method for generating local XQuery queries considered these problems. Also we implement and evaluate a Global XQuery Processor which uses our method.

• Journal of The Korean Association For Science Education
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• v.39 no.5
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• pp.655-668
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• 2019

In this study, we investigated the middle school students' processes of scientific graph construction from the perspective of the problem solving process. Ten 9th graders participated in this study. They constructed a scientific graph based on pictorial data depicting precipitation reaction. The think-aloud method was used in order to investigate their thinking processes deeply. Their activities were videotaped, and semi-structured interviews were also conducted. The analysis of the results revealed that their processes of scientific graph construction could be classified into four types according to the problem solving strategy and the level of representations utilized. Students using the structural strategy succeeded in constructing scientific graph regardless of the level of representation utilized, by analyzing the data and identifying the trend based on the propositional knowledge about the target concept of the graph. Students of random strategy-higher order representation type were able to succeed in constructing scientific graph by systematically analyzing the characteristics of the data using various representations, and considering the meaning of the graph constructed in terms of the scientific context. On the other hand, students of random strategy-lower order representation type failed to construct correct scientific graph by constructing graph in a way of simply connecting points, and checking the processes of graph construction only without considering the scientific context. On the bases of the results, effective methods for improving students' ability to construct scientific graphs are discussed.