• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.79-89
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• 2023

Background and Objectives: The prevalence of dementia is steadily increasing each year, and preceding studies continue to explore the association between dementia and oral health. Dental hygienists require specialized competencies to provide appropriate dental healthcare services, necessitating the development of a tool for the objective measurement of their knowledge levels. This study aimed to develop a knowledge assessment tool for dental hygienists concerning considerations for dental care for patients with dementia. Methods: The study constructed preliminary items based on a literature review and then conducted expert validation, a pilot survey, and the main survey. The main survey was conducted among 220 dental hygienists. Validity and reliability analyses were conducted with the collected data to select the final items, and the correctness rates for each selected item were verified. Results: As a result of the analysis of the collected data, 18 items were eliminated out of a total of 40 preliminary items, leaving a total of 6 factors and 22 items. The Cronbach's α value for the selected items was 0.791. The six factors are as follows: 'Considerations during dental treatment for dementia patients' (5 items), 'medication side effects in dementia patients' (4 items), 'oral care methods for dementia patients' (4 items), 'communication with dementia patients' (4 items), 'psychological reactions of dementia patients' (3 items), and 'guidance for dementia patients' (2 items). The item with the highest correctness rate was item 2 of the 'guidance for dementia patients' category at 98.6%, while the item with the lowest correctness rate was item 2 of the 'psychological reactions of dementia patients' category at 5.9%. Conclusion: This study validated the reliability and validity of the knowledge assessment tool, which lays the foundation for future research on dental hygienists and dementia. It contributes essential data for ongoing education, development of educational programs, and establishing operational guidelines in healthcare institutions.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.51-64
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• 2023

Background: This study was conducted to determine the association with dual jobbing on dental hygienists' job satisfaction and to identify the factors that lead to dual jobs. Methods: This study was an online survey of 110 currently employed dental hygienists conducted during the month of May 2022. To determine job satisfaction, we used the 20-item Korea-Minnesota Satisfaction Questionnaire (K-MSQ). Survey questions related to dual job were adapted and supplemented from the dual job survey instrument for dental hygienists to identify intention to dual job and future intention to dual job. Descriptive statistics, independent t-test, ANOVA and Scheffe's post hoc analysis, and multiple logistic regression were performed. Results: The dual job rate and future dual job rate of the participants in this study were about 27% and 47%, respectively. The means for Intrinsic job satisfaction, Extrinsic job satisfaction, and job satisfaction were 3.44, 3.15, and 3.36, respectively. It was statistically significant that extrinsic job satisfaction increased with increasing position, and intrinsic job satisfaction, extrinsic job satisfaction, and job satisfaction increased with increasing salary. Those currently working dual jobs cited "self-actualization" as a reason for doing so, and those who intended to work dual jobs in the future cited "not being paid enough in their primary job" as a reason. We found that a one-unit increase in intrinsic job satisfaction and job satisfaction increases the odds of future intention to dual job by about 1.07 and 1.05 times, respectively (p<0.05). Conclusion: This study confirmed the influence of dental hygienists' job satisfaction on intention to dual job and future intention to dual job, and self-actualization was found to be the main factor. Therefore, the consideration of dual jobs in the future will affect the improvement of dental hygienists as professionals and the reduction of turnover through better working conditions.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.12
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• pp.1880-1892
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• 2010

The purposes of this study were to: a) provide evidence concerning the effects of emotional intelligence on job outcomes, b) examine the impacts of emotional intelligence on employee-related variables such as 'job satisfaction', 'organizational commitment', 'organizational performance', and 'turnover intention' c) identify the conceptual framework underlying emotional intelligence. A survey was conducted to collect data from foodservice managers (N=231). Statistical analyses were completed using SPSS Win (16.0) for descriptive analysis, reliability analysis, factor analysis, t-test, correlation analysis, cluster analysis and AMOS (16.0) for confirmatory factor analysis and structural equation modeling. The concept of emotional intelligence (EI) has been on the radar screens of many leaders and managers over the last several decades. The emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thinking and interaction with others. The main results of this study were as follows. The four EI (Emotional Intelligence) dimensions correlated significantly with age. The means of job satisfaction score were above the midpoint (3.04 point) scale. The organizational commitment score was above the midpoint (3.41 point) scale and was higher at 'loyalty' factor than 'commitment' factor. The means of organizational performance score were above the midpoint (3.34) scale. The correlations among the four EI (emotional intelligence) factors were significant with job satisfaction; organizational commitment, organizational performance and turnover intention. The test of hypothesis using structural equation modeling found that emotional intelligence produced positive effects on job attitude and job performance. Emotional intelligence enhanced organizational commitment, and in turn, managers' attitude produced positive effects on organizational performance; emotional intelligence also had a direct impact on organizational performance. This study has identified the effect of emotional intelligence on organizational performance and attitudes toward one's job.

• The Korean Journal of Archival Studies
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• no.26
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• pp.103-149
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• 2010

Documentation strategy has been experimented in various subject areas and local regions since late 1980's when it was proposed as archival appraisal and selection methods by archival communities in the United States. Though it was criticized to be too ideal, it needs to shed new light on the potentialities of the strategy for documenting local regions in digital environment. The purpose of this study is to analyse the implementation issues of documentation strategy and to suggest the directions for documenting local regions of Korea through the application of the strategy. The documentation strategy which was developed more than twenty years ago in mostly western countries gives us some implications for documenting local regions even in current digital environments. They are as follows; Firstly, documentation strategy can enhance the value of archivists as well as archives in local regions because archivist should be active shaper of history rather than passive receiver of archives according to the strategy. It can also be a solution for overcoming poor conditions of local archives management in Korea. Secondly, the strategy can encourage cooperation between collecting institutions including museums, libraries, archives, cultural centers, history institutions, etc. in each local region. In the networked environment the cooperation can be achieved more effectively than in traditional environment where the heavy workload of cooperative institutions is needed. Thirdly, the strategy can facilitate solidarity of various groups in local region. According to the analysis of the strategy projects, it is essential to collect their knowledge, passion, and enthusiasm of related groups to effectively implement the strategy. It can also provide a methodology for minor groups of society to document their memories. This study suggests the directions of documenting local regions in consideration of current archival infrastructure of Korean as follows; Firstly, very selective and intensive documentation should be pursued rather than comprehensive one for documenting local regions. Though it is a very political problem to decide what subject has priority for documentation, interests of local community members as well as professional groups should be considered in the decision-making process seriously. Secondly, it is effective to plan integrated representation of local history in the distributed custody of local archives. It would be desirable to implement archival gateway for integrated search and representation of local archives regardless of the location of archives. Thirdly, it is necessary to try digital documentation using Web 2.0 technologies. Documentation strategy as the methodology of selecting and acquiring archives can not avoid subjectivity and prejudices of appraiser completely. To mitigate the problems, open documentation system should be prepared for reflecting different interests of different groups. Fourth, it is desirable to apply a conspectus model used in cooperative collection management of libraries to document local regions digitally. Conspectus can show existing documentation strength and future documentation intensity for each participating institution. Using this, documentation level of each subject area can be set up cooperatively and effectively in the local regions.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.