• Asian Journal for Public Opinion Research
• /
• v.12 no.1
• /
• pp.53-74
• /
• 2024

The proliferation of health misinformation gained momentum amidst the outbreak of the novel coronavirus disease 2019 (COVID-19). People stuck in their homes, without work pressure, regardless of health concerns towards personal, family, or peer groups, consistently demanded information. People became engaged with misinformation while attempting to find health information content. This study used the content analysis method and analyzed 1,154 misinformation stories from four prominent signatories of the International Fact-Checking Network during the pandemic. The study finds the five main categories of misinformation related to the COVID-19 pandemic. These are 1) the severity of the virus, 2) cure, prevention, and treatment, 3) myths and rumors about vaccines, 4) health authorities' guidelines, and 5) personal and social impacts. Various sub-categories supported the content characteristics of these categories. The study also analyzed the emotional valence of health misinformation. It was found that misinformation containing negative sentiments got higher engagement during the pandemic. Positive and neutral sentiment misinformation has less reach. Surprise, fear, and anger/aggressive emotions highly affected people during the pandemic; in general, people and social media users warning people to safeguard themselves from COVID-19 and creating a confusing state were found as the primary motivation behind the propagation of misinformation. The present study offers valuable perspectives on the mechanisms underlying the spread of health-related misinformation amidst the COVID-19 outbreak. It highlights the significance of discerning the accuracy of information and the feelings it conveys in minimizing the adverse effects on the well-being of public health.

• Quality Improvement in Health Care
• /
• v.30 no.1
• /
• pp.132-152
• /
• 2024

Purpose: This study aimed to identify and evaluate interprofessional education (IPE) interventions for healthcare professional students in East Asian countries. Methods: The reporting of this study followed the Preferred Reporting Items of Systematic Reviews and Meta-Analysis guidelines. A literature search was conducted using seven electronic databases: PubMed, EMBASE, CINAHL, Scopus, Web of Science, ERIC, and ProQuest Dissertations & Theses Global. Joanna Briggs Institute Critical Appraisal Checklists were also used to appraise the quality of the included studies. The outcomes of IPE interventions were classified based on a modified Kirkpatrick model. Results: This review included 30 studies predominantly conducted in Singapore, South Korea, and Taiwan. The prevalent research design was a one-group pre-posttest design, and most IPE interventions occurred as single events. Approximately 70% of the studies involved students from two healthcare professions, mainly nursing and medicine. Simulations, group discussions, and lectures have emerged as the most common teaching methodologies, with almost half of the studies leveraging a combination of these techniques. The IPE content primarily focused on interprofessional teamwork, communication, and clinical patient care situations; these included the management of septic shock. The effectiveness of the IPE interventions was mainly evaluated through self-reported measures, indicating improvements in attitudes, perceptions, knowledge, and skills, aligning with Level 2 of the modified Kirkpatrick model. Nonetheless, the reviewed studies did not assess changes in the participants' behavior and patient results. Conclusion: IPE interventions promise to enhance interprofessional collaboration and communication skills among health professional students. Future studies should implement rigorous designs to assess the effectiveness of IPE interventions. Moreover, when designing IPE interventions, researchers and educators should consider the role of cultural characteristics in East Asian countries.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.179-192
• /
• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of Intelligence and Information Systems
• /
• v.16 no.3
• /
• pp.181-199
• /
• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.1
• /
• pp.70-79
• /
• 2014

Following the technology developments of traffic network and communication for the wide area, the importance of the cooperation system to vitalize the wide area economy is increasing. Therefore, in this study, DongNam Kwon industry information system is established for the industrial information sharing based on GIS in the DongNam Kwon wide area economy. The DongNam Kwon is an industrial integration area centered with the manufacturing so that the operation of effective industrial cluster and cooperation systems are required across the administrational boundaries. To establish the database of the information, the information system was established utilizing already established industrial databases in Busan, Ulsan and Gyeongnam. But, various issues caused by the discordances among the data of each local government and the insufficiency of GIS based location information have been found. According to the analysis, the standardization considering the courses of collection, distributions and utilization are required immediately to solve the issues. This study establishes an 2-way industrial information system enabling the information creation and the phased approach between the administrator and the user in the bi-directions on the web by utilizing cadastral and numerical maps. The result of this study would have a meaning in providing a fundamental frame for cooperative responses and cooperation system for DongNam Kwon's industrial promotion using industrial information sharing.

• Journal of the Korea Computer Graphics Society
• /
• v.14 no.4
• /
• pp.35-39
• /
• 2008

A computer supported collaborative system provides with a shared virtual workspace over the Internet where its remote users cooperate in order to achieve their goals by overcoming problems caused by distance and time. VRMMS (Virtual Reality Molecular Modeling System) [1] is a VR based collaborative system where biologists can remotely participate in and exercise molecular modeling tasks such as viewing three dimensional structures of molecular models, confirming results of molecular simulations and providing with feedbacks for the next simulations. Biologists can utilize VRMMS in executing molecular simulations. However, first-time users and beginners need to spend some time for studying and practicing in order to skillfully manipulate molecular models and the system. The best way to resolve the problem is to have a face-to-face session of teaching and learning VRMMS. However, it is not practically recommended in the sense that the users are remotely located. It follows that the learning time could last longer than desired. In this paper, we propose to use Second Life [2] combining with VRMMS for removing the problem. It can be used in building a shared workplace over the Internet where molecular simulations using VRMMS can be exercised, taught, learned and practiced. Through the web, users can collaborate with each other using VRMMS. Their avatars and tools of molecular simulations can be remotely utilized in order to provide with senses of 'being there' to the remote users. The users can discuss, teach and learn over the Internet. The shared workspaces for discussion and education are designed and implemented in Second Life. Since the activities in Second Life and VRMMS are designed to realistic, the system is expected to help users in improving their learning and experimental performances.

• Journal of Korean Library and Information Science Society
• /
• v.49 no.3
• /
• pp.219-240
• /
• 2018

The study analyzed the characteristics of two library and information science journals indexed by Web of Science (WoS) and Scopus in 2015 along with two of their non-indexed sister journals. The study also analyzed the characteristics of a SCIE- and Scopus-indexed oceanographic journal published domestically along with its sister journal indexed by Scopus only. In addition, the study collected and analyzed the articles published during 2014 and 2015 in these 6 journals as well as the citations they received in 2016. By comparing the characteristics and publication data of indexed journals and their sister journals, the study aimed to identify the properties of WoS-indexed journals that may be helpful in enhancing international journal publication. The findings from the analysis of data can be summarized as follows: WoS-indexed journals publish more frequently, focus mainly on articles, are authored by researchers from many countries, and have higher quality papers that receive more citations than their non-WoS-indexed sister journals. The study also found higher rates of co-authored papers in WoS indexed journals, which were amplified in LIS journals. This is an important factor to consider for Korean LIS journals that are dominated by singe-author articles when they prepare to be indexed in WoS.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.17-35
• /
• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.1
• /
• pp.189-193
• /
• 2013

Many epidemiological studies in Asian populations have investigated associations between the Arg399Gln gene polymorphism of X-ray repair cross complementing gene 1 (XRCC1) and risk of cervical carcinoma, but no conclusions have been available because of controversial results. Therefore a meta-analysis was conducted for clarification. Relevant studies were identified by searching the Pubmed, Embase, the Web of Science, Cochrane Collaboration's database, Chinese National Knowledge Infrastructure (CNKI), Wanfang database and China Biological Medicinse (CBM) until September, 2012. A total of eight studies were included in the present meta-analysis, which described 1,759 cervical carcinoma cases and 2,497 controls. Odds ratios (ORs) and corresponding 95% confidence intervals (95%CIs) as effect size were calculated by fixed-effect or random-effect models. The overall results indicated that the XRCC1-399G/A polymorphism was marginally associated with cervical carcinoma in Asians: OR (95%CI): 1.16 (1.07, 1.26) in the G/A vs G/G inheritance model, 1.24 (0.87, 1.76)in A/A vs G/G inheritance model, 1.13 (1.01, 1.27) in the dominant inheritance model and 1.18 (0.94, 1.47) in the recessive inheritance model. Subgroup analyses on sample size showed no significant correlation in the small-sample size group but the large-sample size group was consistent with the outcomes of overall meta-analysis. In the subgroup analysis by regions, we only found significant association under the G/A vs G/G inheritance model in the Chinese population. For the non-Chinese populations, no correlation was detected in any genetic inheritance model. In the Asian populations, XRCC1-399G/A gene polymorphism was implied to be associated with cervical carcinoma.

• The Journal of Society for e-Business Studies
• /
• v.17 no.2
• /
• pp.129-147
• /
• 2012

Collaborative filtering (CF), one of the most successful recommendation techniques, has been used in a number of different applications such as recommending web pages, movies, music, articles and products. One of the critical issues in CF is why recommendation performances are different depending on application domains. However, prior literatures have focused on only data characteristics to explain the origin of the difference. Scant attentions have been paid to provide systematic explanation on the issue. To fill this research gap, this study attempts to systematically explain why recommendation performances are different using structural indexes of social network. For this purpose, we developed hypotheses regarding the relationships between structural indexes of social network and recommendation performance of collaboration filtering, and empirically tested them. Results of this study showed that density and inconclusiveness positively affected recommendation performance while clustering coefficient negatively affected it. This study can be used as stepping stone for understanding collaborative filtering recommendation performance. Furthermore, it might be helpful for managers to decide whether they adopt recommendation systems.