• Journal of Preventive Medicine and Public Health
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• v.28 no.4 s.51
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• pp.795-807
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• 1995

This study was carried out from 1993 to 1994 in the small industries in Incheon. The objectives of this study was in order to estimate the quitting rate, to identify its relating factors and to propose effective quitting management policy in the small industries. The results were as follows ; 1. The quitting rate of 266 study workers was 42.1%(112 workers). 2. Age, working duration, position, marrital status were significant difference between the quitting group and the non - quitting group. In the quitting group, mean age was young, working duration was short, general employees and unmarried workers were many compared with the non - quitting group. 3. In the industry characteristics, total assets, total assets, sales per person, establishment duration and occupational health and safely status were significant difference between the quitting group and the non - quitting group. In the quitting group, total assets, total sales and sales per person were little, establishment duration of company was short and occupational health and safety status were poor compared with the non - quitting group. 4. In the quitting group, worker's response to employer's disposal about health and safety was more passive and the relation to employer with employee was significantly poor compared with the non - quitting group. 5. Multiple logistic regression analysis of quitting against family income per person, working duration, relation to employer with employee, occupational health and safety status in industry, worker's response to employer's disposal about health and safety and sales per person was done. Working duration, occupational health and safety status, worker's response to employer'1 disposal about health and safety were significant explainatory variables for quitting. Above results showed that the quitting rate was high and it was significant difference between the quitting group and non : quitting group according to characteristics of workers and of industries. Especially, it suggested that working duration, occupational health and safety status and worker's response to employer's disposal about health and safety were significant quitting factor. Therefore, it should be reflected in the quitting management and the policy of steady employment.

• Asian Journal of Business Environment
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• v.2 no.1
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• pp.25-34
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• 2012

It is possible to use pallet for forwarding as chinese cabbages and radishes are general large-scale trading items at the agricultural wholesale market though, however, most of these are forwarded as it have packed in net bags or in P·E bags. Thus, it is still hard for palletizing. The type of packing the product in the net bag makes it difficult for palletizing. It is not a stable shape enough and easily collapsed for pallet loading. Because of this collapsibility, the corrugated cardboard box is being used to enhance forwarding efficiency, but the existing corrugated cardboard box could be crushed easily by moist what is from the agricultural product's property and it also could be squashed by the mass of the loaded box layers on itself. In contrary, the functional waterproof corrugated cardboard box is not collapsed through palletizing and it is efficient for product management with it's ventilation function in respond to pre-cooling effect. Furthermore, because it has various functional shapes as the open type, the partition type and so on, it is effective for maintaining freshness of the product and standardizing the distribution of agricultural product. It is well-known that it is possible to introduce this box to cargo-works of agricultural product. Consequently, the recognition of main distributors about the pallet distribution of the chinese cabbage and the radish was apprehended in this study for activating mechanization of loading and unloading. The survey was conducted to the main distributors such as the forwarder, the auction dealer and the commission merchant with Garak-dong wholesale market as the center. The appropriate packing materials and problems of the existing method for loading and unloading were derived through the survey. Especially, it was focused on analyzing the difference of recognition between the subject groups for the way of using waterproof cardboard corrugated box to deal with the difficult product for packing in normal corrugated box because of the box's absorption of moist from the agricultural product like a chinese cabbage and a radish. Total In the cases of the forwarders and the commission merchants, the net was highly responded as 45%, 74% from each groups for the best packing material for mechanization of distribution and the waterproof corrugated cardboard box was responded as 20%, 22% from each groups as much preferable than multi-stage wooden box. However, for the radish, the waterproof corrugated cardboard box was the best material as 56%, and the auction trader group supported it for 80%. So, the using the waterproof corrugated cardboard box for mechanization of distribution was negative for the chinese cabbage, but it was positive for the radish. The average was 2.42, the standard deviation was 1.24. The negative response(about 55%) was prevailing more than positive response(about 23%). It could be analyzed that even there was the positive recognition for using the waterproof corrugated cardboard box for the radish though the preference for low price of net bag in the chinese cabbage forwarding procedure. Still now, it seems that is a burden for using the waterproof corrugated cardboard box with high price. In the analysis on the recognition differences about using the waterproof corrugated cardboard box for the chinese cabbages and the radish between the forwarders and the commission merchants, generally the negative recognition was prevailing, but the forwarders(2.696) were more positive for using the waterproof corrugated cardboard box than the commission merchants(2.145).

• Journal of Nutrition and Health
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• v.56 no.2
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• pp.155-167
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• 2023

Purpose: This study investigates the association between seafood consumption and frailty according to gender in the Korean elderly. Methods: Cross-sectional data from the Seventh (2016-2018) Korea National Health and Nutrition Examination Survey was procured for this study. Data from 3,675 subjects (1,643 men and 2,032 women) aged ≥ 65 years were analyzed. Levels of seafood intake were assessed by a one-day 24-hour dietary recall, and subjects were classified into three tertiles by gender according to frailty phenotype: robust, pre-frail, and frail. Multinomial logistic regression analysis was performed to clarify the association between seafood consumption and frailty for each gender. Results: The prevalence of frailty was determined as 13.4% for men and 29.7% for women. Participants with a higher seafood intake had higher intakes of grains, fruits, and vegetables, while the intake of meat was significantly lower. In both men and women, the group with higher seafood intake showed higher energy and micronutrient intakes. The frail prevalence and frailty score were significantly low in the highest tertiles of seafood consumption compared to the lowest tertile in men and women (p < 0.001). After adjusting for confounder, the highest tertile of seafood consumption showed a decreased risk of frailty compared to the lowest tertile only in women (hazard ratio [HR], 0.50; 95% confidence interval [CI], 0.32-0.78; p-trend = 0.008 vs. HR, 0.52; 95% CI, 0.32-0.83; p-trend = 0.008; respectively). Conclusion: Results of this study suggest that seafood consumption potentially decreases the risk of frailty in the elderly.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.702-713
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• 2023

Purpose: Depression is a prevalent mental health concern globally including South Korea. Given the growing interest in the relationship between diet and mental health, this study aimed to investigate the association between seafood consumption and depression among Korean adults. Methods: A cross-sectional analysis was conducted using data from the Korea National Health and Nutrition Examination Survey (KNHANES, 2014-2020). The study included 18,149 participants (7,541 men and 10,608 women) aged 19 years and older who completed the Patient Health Questionnaire (PHQ-9). Seafood intake levels were assessed using a oneday 24-hour dietary recall, and participants were categorized into three tertiles by gender. Depression status was determined using the PHQ-9 scores and the self-report of the doctor's diagnosis and treatment. Multivariable logistic regression analysis was performed to assess the association between seafood consumption and depression in both genders. Results: Participants with a higher seafood intake had a significantly lower nutritional density of total fat, while the nutritional density of omega-3 polyunsaturated fatty acids was significantly higher. The prevalence of depression was significantly lower in the highest tertile of seafood consumption compared to the lowest tertile in both men (p < 0.001) and women (p < 0.001). After adjusting for confounding factors, the highest tertile of seafood consumption demonstrated a decreased risk of depression compared to the lowest tertile in men (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.51-0.99; p-trend = 0.020) and women (OR, 0.73; 95% CI, 0.59-0.91; p-trend = 0.004). Conclusion: The findings of this study suggest that consuming seafood rich in omega-3 fatty acids may potentially reduce the risk of depression in the adult population.

• Health Policy and Management
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• v.34 no.2
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• pp.141-155
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• 2024

Background: This study aimed to identified the relationship between the risk of obstructive sleep apnea, subjective health, and health-related quality of life among the middle-aged and elderly population in Korea. Methods: Adults aged 40 or older were extracted from the total 22,559 respondents to the 2019-2020 Korea National Health and Nutrition Examination Survey VIII, and secondary analysis was conducted on a total of 6,659 middle-aged and elderly people with no missing values. Logistic regression analysis and multiple regression analysis were conducted to examine the relationship between obstructive sleep apnea risk factors and subjective health as well as quality of life. Results: The subjective health status decline in the high-risk group compared to the non-risk group for obstructive sleep apnea was statistically significantly higher, with an odds ratio of 1.84 (p<0.001). The health-related quality of life was also statistically significantly lower by 0.02 points (β, -0.02; p<0.001). As a result of subgroup analysis on specific variables, the association between the risk of obstructive sleep apnea and subjective health and health-related quality of life was statistically significant depending on gender, sleep time, presence of depression, household income, and number of household members. Based on the obstructive sleep apnea risk group, women had a higher correlation with low subjective health and lower health-related quality of life scores than men. Sleeping time of more than 8 hours or less than 6 hours was more associated with low subjective health and lower health-related quality of life score than sleeping time of 6-8 hours. Patients with depression were more likely to have low subjective health than those without depression. The lower the household income level and the smaller the number of household members, the higher the association with low subjective health and the lower the health-related quality of life score. Conclusion: It is essential to recognize that the risk of obstructive sleep apnea not only directly affects sleep disorders but also impacts individuals' subjective health and quality of life. Consequently, social support and education should be provided to raise awareness of this issue. Particularly, programs for preventing and managing obstructive sleep apnea should target vulnerable groups such as women, individuals in single-person households, low household income, and those with depression, aiming to improve their subjective health and quality of life.

• Health Policy and Management
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• v.34 no.2
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• pp.211-221
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• 2024

Background: The public health crisis caused by coronavirus disease 2019 emphasizes the need to expand and strengthen public hospitals. However, the overall perception of public hospitals remains negative. This negative perception can hinder the roles and functions of public hospitals, so this study aims to analyze the factors affecting negative perceptions of public hospitals. Methods: We used data from a survey on the public healthcare of Chungcheongbuk-do residents conducted by the Chungcheongbuk-do Public Health Policy Institute, and 1,916 adults aged 19 or older who responded to the survey were included in the study. Logistic regression analysis was used to analyze the impact of experiences with public hospitals use and evaluations of public healthcare and public hospital policies on the negative perception of public hospitals. Results: The experience of not using public hospitals (adjusted odds ratio [aOR], 1.69; 95% confidence interval [CI], 1.04-2.74) and negative evaluations of public healthcare and public hospital policies were found to significantly impact negative perceptions of public hospitals. In public healthcare policies, negative evaluations of the provision of essential medical care (aOR, 4.14; 95% CI, 2.59-6.62), regional disparities (aOR, 1.59; 95% CI, 1.02-2.49), coverage (aOR, 1.99; 95% CI, 1.25-3.16), and quality of care (aOR, 2.39; 95% CI, 1.50-3.80) were significantly associated with negative perceptions of public hospitals. In public hospital policies, negative evaluations of facilities and equipment (aOR, 3.74, 95% CI, 2.36-5.94), medical specialties and services (aOR, 1.91; 95% CI, 1.21-3.01), and quality of medical service (aOR, 2.71; 95% CI, 1.72-4.25) were also significantly associated with negative perceptions of public hospitals. Conclusion: This study emphasizes the need to improve perceptions of public hospitals by considering the experience with public hospitals use and evaluation of public healthcare and public hospital policies.

• Health Policy and Management
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• v.34 no.2
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• pp.185-195
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• 2024

Background: Disabled people have particularly restricted access to health care. In response to this, the pilot project for the general physician (GP) system for disabled people was implemented in 2018, based on the rights of people with disability to the Health Act in South Korea. However, its participants were 0.2% among the total of those with severe disabilities in 2021. Therefore, this study examined the factors related to registering with a GP and the access level to its services to suggest implications for activating the participation of disabled people. Methods: We analyzed factors affecting the registration with a GP and the number of using the services among the participants of the GP system during May 2018 and December 2021 by conducting hierarchical logistic regression and hierarchical regression. The data were linked with the national health insurance data to examine various predictors, including disability types, socioeconomic status, health status, and GP registration. Results: As a result of analyzing the factors affecting whether or not to register for the pilot project, those with disabilities (physical disabilities, brain lesions, visual, intellectual, mental, and autistic disability) eligible for disability care (odds ratio [OR], 4.157) than other disability, and those living in metropolitan (OR, 4.330) or cities (OR, 3.332) than rural residences were highly likely to enroll the pilot study. Health-related variables also predicted the registration status of the pilot project. The predictors related to GP enrollment types (membership type: general health or disability care, GP's affiliation: clinics or hospitals) significantly influenced levels of access to services. Conclusion: It is necessary to develop the GP project for disabled people by considering the variation in types of disability, residences, and health. Further study will be needed to investigate the impact of GPs on the level of participation among disabled people.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.2
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• pp.27-44
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• 2024

Objectives: This study aimed to provide foundational data for preventing adolescents smoking by analyzing the relationship between adolescents' lifestyles and smoking experiences and identifying influencing factors. Methods: Secondary data analysis was conducted using the 17th (2021) Youth Health Behavior Survey data, encompassing 54,848 students from 796 schools. Variables included general characteristics, smoking status, lifestyle habits, physical activity, sleep patterns, and stress perception. Frequency analysis was used to examine general characteristics, while further analysis employed frequency analysis and the Pearson Chi-square test to compare lifestyle differences based on smoking presence. Multinomial logistic regression analysis was employed to determine factors influencing smoking experience, with IBM SPSS Statistics 28 used for all analyses at a significance level of p<.05. Results: Analysis revealed with general characteristics that the group with smoking experience exhibited a higher proportion of male students (67.4%) compared to the non-smoking group (50.1%) (p<.001). Analysis revealed that the smoking group was more likely to skip breakfast (27.7%), not consume fruit (17.8%), and consume fast food more than three times daily (0.9%). Furthermore, a higher percentage of smokers engaged in 60 minutes or more of breathless physical activity (8.4%) seven times a week, reported insufficient fatigue recovery through sleep (21.6%), and experienced very severe normal stress (17.2%) (p<.001). Analysis of the relationship between lifestyle and smoking indicated increased likelihood of smoking with zero breakfast consumption (OR=1.759, p<.001) and increased fruit consumption (OR=1.921, p<.001), while zero fast food consumption decreased smoking likelihood (OR=0.206, p<.001). Adequate sleep-related fatigue recovery reduced smoking likelihood (OR=0.458, p<.001), whereas increased stress elevated it (OR=1.260, p<.05). Conclusion: Adolescents' lifestyle habits significantly correlated with their smoking experiences, highlighting the necessity of considering lifestyle factors in smoking prevention strategies. This study provides crucial insights for promoting healthy lifestyle changes to prevent smoking among youth.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.