1.A Three-Year Study of the Effectiveness of Hand-Hygiene Protocol Implementation at a University Hospital.
Oh Mee KWEON ; Eunsuk PARK ; Dongsuk LEE ; Ju Hyun LEE ; Eun Jin HA ; Dongeun YONG ; Jun Yong CHOI ; Ki Hwan KIM ; Chul LEE ; Kyungwon LEE
Korean Journal of Nosocomial Infection Control 2012;17(2):53-60
BACKGROUND: Compliance with hand hygiene protocols is one of the simplest ways to prevent healthcare-associated infections (HAIs). Hand hygiene is influenced by individual habits and beliefs, as well as by local organizational culture practices. This study was performed in order to increase the rate of compliance to hand hygiene through changes in the organizational culture. METHODS: From 2009 through 2011, this study was performed in a 2,000-bed tertiary-care university hospital with more than 6,000 employees. The program was implemented mainly by team activities, and the leadership and hand hygiene steering committee members supported them. Goals for planning, intervention, and evaluation of the compliance rate for hand hygiene were made annually in the hospital. RESULTS: The rate of compliance to hand hygiene increased significantly each year (43.8% in 2008, 75.3% in 2009, 80.7% in 2010, and 83.2% in 2011). The detection rate of vancomycin-resistant Enterococcus (VRE) and the incidence of healthcare-associated Staphylococcus aureus bacteremia decreased. CONCLUSION: The rate of compliance to hand hygiene was remarkably improved, and it continuously increased through systematic and continuous changes in the organizational culture. In addition, the detection rate of VRE and incidence of S. aureus bacteremia decreased. These results show that hand hygiene is an important factor for preventing HAIs.
Bacteremia
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Committee Membership
;
Compliance
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Enterococcus
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Hand Hygiene
;
Incidence
;
Organizational Culture
;
Staphylococcus aureus
2.Needs for Development of IT-based Nutritional Management Program for Women with Gestational Diabetes Mellitus.
Chan Jung HAN ; Sun Young LIM ; Eunsuk OH ; Yoon Hee CHOI ; Kun Ho YOON ; Jin Hee LEE
Korean Journal of Community Nutrition 2017;22(3):207-217
OBJECTIVES: The aim of this study was to examine self-management status, nutritional knowledge, barrier factors in dietary management and needs of nutritional management program for women with Gestational Diabetes Mellitus (GDM). METHODS: A total of 100 women with GDM were recruited from secondary and tertiary hospitals in Seoul. The questionnaire composed of general characteristics, status of self-management, dietary habits, nutrition knowledge, barrier factors in dietary management, needs for nutrition information contents and nutritional management programs. Data were collected by a self-administered questionnaire. All data were statistically analyzed using student's t-test and chi-square test using SAS 9.3. RESULTS: About 35% of the subjects reported that they practiced medical nutrition and exercise therapy for GDM control. The main sources of nutrition information were ‘internet (50.0%)’ and ‘expert advice (45.0%)’. More than 70% of the subjects experienced nutrition education. The mean score of nutrition knowledge was 7.5 point out of 10, and only about half of the subjects were reported to be correctly aware of some questions such as ‘the cause of ketosis’, ‘the goal of nutrition management for GDM’, ‘the importance of sugar restriction on breakfast’. The major obstructive factors in dietary management were ‘eating more than planned when dining out’, ‘finding the appropriate menu when dining out’. The preferred nutrition information contents in developing management program were ‘nutritional information of food’, ‘recommended food by major nutrients’, ‘the relationship between blood glucose and food’, ‘tips on menu selection at eating out’. The subjects reported that they need management program such as ‘example of menu by calorie prescription’, ‘recommended weight gain guide’, ‘meal recording and dietary assessment’, ‘expert recommendation’, ‘sharing know-how’. CONCLUSIONS: Based on the results of this study, it is necessary to develop a program that provide personalized information by identifying the individual characteristics of the subjects and expert feedback function through various information and nutrition information contents that can be used in real life.
Blood Glucose
;
Diabetes, Gestational*
;
Eating
;
Education
;
Exercise Therapy
;
Female
;
Food Habits
;
Humans
;
Needs Assessment
;
Nutritional Status
;
Pregnancy
;
Self Care
;
Seoul
;
Tertiary Care Centers
;
Weight Gain
3.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
4.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
5.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
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