• Journal of Korea Entertainment Industry Association
• /
• v.13 no.1
• /
• pp.157-166
• /
• 2019

The purpose of this study was to investigate the level of professor trust, achievement motivation, and self-efficacy of health college students in G university, and to analyze the relationship between these variables and its influential factors to provide basic data on improvement of teaching methods and academic achievement. The study period was March 1 through 30, 2018. Study subjects were 276 health college students in G university located in G. City. The survey was conducted by the researcher, described the purpose of the study and distributed and collected the questionnaires. The questionnaire consisted of professor trust 27 items, achievement motivation 28 items and self-efficacy 24 items. As a result, professor trust was higher when the grade was lower and the degree of major satisfaction was better, and in order of the division of the health administration, the department of speech-language therapy, and the department of nursing. Achievement motivation was higher in the first grade than in 4th grade and when the degree of major satisfaction was better. Self-efficacy was higher in order of higher grades. Professor trust, achievement motivation, and self-efficacy were correlated with each other, and achievement motivation and self-efficacy influenced the professor trust. The result of this study suggests that the achievement motivation and self-efficacy of college students will be improved based on the mutual trust among the members of the university.

• Clinical and Experimental Pediatrics
• /
• v.49 no.5
• /
• pp.507-512
• /
• 2006

Purpose : Early detection and intervention of hearing impairment is believed to improve speech and language development and behavior of children. The aim of this preliminary study was to determine the prevalence of hearing impairments, and to identify the association of risk factors relating to refer response in high risk neonates who were screened using distortion product otoacoustic emissions (DPOAE). Methods : The subjects included 871 neonates who were admitted to the neonatal intensive care unit of the Pediatric Department in Soonchunhyang University Bucheon Hospital from May, 2001 to December, 2004. They were screened using DPOAE. Based on DPOAE, we divided the neonates in two groups : 'Pass' and 'Refer'. The differences in risk factors between the pass group and the refer group were analyzed. Results : The incidence of the refer group was 12.1 percent(106 out of 871). The bilateral refer rate was 5.4 percent(47 out of 871). And the unilateral refer rate was 6.7 percent(59 out of 871). Gender, birth place, family history of hearing loss, small/large for gestational age, obstetrical factor, hyperbilirubinemia and use of gentamicin were not statistically related to the refer rate. Statistically related to refer rate were birth weight, resuscitated neonates, Apgar score, craniofacial anomaly, mechanical ventilator application, sepsis, using of vancomycin(P<0.05). The prevalence of hearing impairment (${\geq}60dB$) in this study was 2 percent(18 out of 871). Conclusion : This study showed a higher prevalence of hearing impairment in high-risk neonates. Thus neonatal hearing screening should be carried out in high-risk neonates.

• The Journal of Korean society of community based occupational therapy
• /
• v.9 no.2
• /
• pp.33-42
• /
• 2019

Objective : The aim of this study was to evaluate the current knowledge regarding constraint-induced movement therapy (CIMT) and its application in clinical practice by physiatrists and therapists in pediatric rehabilitation area in Korea. Methods : Online survey via E-mails was sent to a total of 510 members (204 physiatrists and 306 therapists) of the Korean Society of Pediatric Rehabilitation and Developmental Medicine (KSPRDM). Results : The response rate was 35.1% (179 of 510). A total of 179 questionnaires was completed by 39 physiatrists, 89 physiotherapists, 48 occupational therapists, and 3 speech therapists. 45.8% of responders had worked over 6 years in the pediatric rehabilitation setting and a total of 58.1% (n=104) of the sample had used CIMT. The main limitations of clinically applying CIMT included limited staff and inappropriate clinical setting (35.1%, n=61), lack of understanding (19.5%, n=34), and developmental issues of function on the unaffected side (13.8%, n=24). The cooperation of patients (77.6%, n=76), cognitive/behavioral factors (42.9%, n=42), and cooperation of caregivers (25.5%, n=25) were the 3 major concerns that could be limitations with CIMT. Conclusions : Although considerable evidence supports the use of CIMT, many of physiatrist and therapists do not apply this method in practice. The improvement of limitations is necessary for wide use of CIMT in clinical practice in Korea.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.