• Journal of Preventive Medicine and Public Health
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• v.54 no.5
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• pp.330-339
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• 2021

Objectives: Tobacco control in Indonesia is very lenient compared to international standards. This study explored the perspectives of tobacco control stakeholders (TCSs) on the likelihood of advancing tobacco marketing regulation in Indonesia. Methods: Data were collected from TCSs who were members of the Indonesia Tobacco Control Network group in a modified Delphi study. We collected the data in 2 waves using a questionnaire that comprised a set of closed and open-ended questions. For this paper, we analysed 2 of the 3 sections of the questionnaire: (1) tobacco advertising, promotions, and sponsorship (TAPS) bans, and (2) marketing and retailing regulations. We conducted a descriptive analysis of the scores using Stata/IC.13 and summarised the comments for each item. Results: The TCSs viewed the measures/strategies across all aspects of TAPS and tobacco marketing regulation as highly desirable, but provided varied responses on their feasibility. They rated political feasibility lower than technical feasibility for most measures. Advancing TAPS measures and prohibition of selling to minors were considered more attainable by sub-national governments, while prohibition of tobacco corporate social responsibility was considered as the least feasible measure in the next 5 years. Conclusions: Despite little optimism for substantial national-level change, there is a positive expectation that sub-national governments will strengthen their tobacco control regulation. It is paramount that the government reduce tobacco industry leverage by implementing Article 5.3 of the World Health Organization Framework Convention on Tobacco Control. Extending advocacy networks beyond tobacco control groups and framing tobacco control more effectively are necessary steps.

• Journal of Korean Clinical Nursing Research
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• v.27 no.3
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• pp.279-293
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• 2021

Purpose: The purpose of this study was to develop a Korean nursing work environment scale for critical care nurses (KNWES-CCN) and verify its validity and reliability. Methods: A total of 46 preliminary items were selected using content validity analysis of experts on 64 candidate items derived through literature reviews and in-depth interviews with critical care nurses. 535 critical care nurses from 21 hospitals responded to the preliminary questionnaire from February to March 2021. The collected data were analysed using construct, convergent and discriminant validities, and internal consistency and test-retest reliability. Results: The 23 items in 4 factors accounted for 55.6% of the total variance were identified through item analysis and exploratory factor analysis (EFA). EFA was performed with maximum likelihood method including direct oblimin method. In the confirmatory factor analysis, KNWES-CCN consisted of 21 items in 4 factors by deleting the items that were not meet the condition that the factor loading over .50 or the squared multiple correlation over .30. This model was considered to be suitable because it satisfied the fit index and acceptable criteria of the model [𝒳²=440.47 (p<.001), CMIN/DF=2.41, GFI=.86, SRMR=.06, RMSEA=.07, TLI=.90, CFI=.91]. The item total correlation values ranged form .32 to .73 and its internal consistency was Cronbach's α=.92. The reliability of the test-retest correlation coefficient was .72 and the intra-class correlation coefficient was .83. Conclusion: The KNWES-CCN showed good validity and reliability. Therefore, it is expected that the use of this scale would measure and improve nursing work environment for critical care nurses in Korea.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.363-376
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• 2022

An area under construction for a living facility collapsed around 12:48 KST on 13 January 2021 in Sa-dong, Ansan-si, Gyeonggi-do. There were no casualties due to the rapid evacuation measure, but part of the temporary retaining facility collapsed, and several cracks occurred in the adjacent road on the south side. This study used the potential of synthetic aperture radar (SAR) satellite for surface property changes that lies in backscattering characteristic to map the collapsed structure. The interferometric SAR technique can make a direct measurement of the decorrelation among different acquisition dates by integrating both amplitude and phase information. The damage proxy map (DPM) technique has been employed using four high-resolution Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) data spanning from 2020 to 2021 during ascending observation to analyze the collapse of the construction. DPM relies on the difference of pre- and co-event interferometric coherences to depict anomalous changes that indicate collapsed structure in the study area. The DPMs were displayed in a color scale that indicates an increasingly more significant ground surface change in the area covered by the pixels, depicting the collapsed structure. Therefore, the DPM technique with SAR data can be used for damage assessment with accurate and comprehensive detection after an event. In addition, we classify the amplitude information using support vector machine (SVM) and maximum likelihood classification algorithms. An investigation committee was formed to determine the cause of the collapse of the retaining wall and to suggest technical and institutional measures and alternatives to prevent similar incidents from reoccurring. The report from the committee revealed that the incident was caused by a combination of factors that were not carried out properly.

• Journal of Preventive Medicine and Public Health
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• v.55 no.6
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• pp.568-577
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• 2022

Objectives: Many studies have shown that social distancing, as a non-pharmaceutical intervention (NPI) that is one of the various measures against coronavirus disease 2019 (COVID-19), is an effective preventive measure to suppress the spread of infectious diseases. This study explored the relationships between traditional health-related behaviors in Korea and social distancing practices during the COVID-19 pandemic. Methods: Data were obtained from the 2020 Community Health Survey conducted by the Korea Disease Control and Prevention Agency (n=98 149). The dependent variable was the degree of social distancing practice to cope with the COVID-19 epidemic. Independent variables included health-risk behaviors and health-promoting behaviors. The moderators were vaccination and unmet medical needs. Predictors affecting the practice of social distancing were identified through hierarchical multiple logistic regression analysis. Results: Smokers (adjusted odds ratio [aOR], 0.924) and frequent drinkers (aOR, 0.933) were more likely not to practice social distancing. A greater degree of physical activity was associated with a higher likelihood of practicing social distancing (aOR, 1.029). People who were vaccinated against influenza were more likely to practice social distancing than those who were not (aOR, 1.150). However, people with unmet medical needs were less likely to practice social distancing than those who did not experience unmet medical needs (aOR, 0.757). Conclusions: Social distancing practices were related to traditional health behaviors such as smoking, drinking, and physical activity. Their patterns showed a clustering effect of health inequality. Therefore, when establishing a strategy to strengthen social distancing, a strategy to protect the vulnerable should be considered concomitantly.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.1
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• pp.5-16
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• 2023

Purpose: This methodological study developed a scale that reflects the social support characteristics of nursing students who have experienced clinical practice and verified their reliability and validity. Methods: The 37 initial items of the scale were derived based on the results of a previous study that analyzed the concept of nursing students' social support. The initial items were revised through content validity verification, and 29 preliminary items were finally selected. Data were collected from 220 students enrolled in a nursing department in Jeollabuk-do, who had clinical practice experience, and 205 surveys were used for the final analysis. The data collection period was from March 11 to April 26, 2022. An exploratory factor analysis was performed using maximum likelihood factor extraction and varimax rotation. Results: The social support scale for nursing students who have experienced clinical practice consisted of a total of three sub-factors and 17 items. The three sub-factors were 'support from family and friends' (eight items), 'support from school, professors, and clinical instructors' (seven items) and 'support from department seniors' (two items). The reliability of the developed scale was found to be high, with a Cronbach's alpha of .93. As a result of verifying the criterion validity of the developed scale, the correlation between the criterion tool and the scale developed in this study was statistically significant. Conclusion: The significance of this study is that it developed a scale to measure social support for the first time among nursing students in Korea.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.35 no.1
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• pp.75-81
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• 2024

Objectives: Although adolescents appear less vulnerable to coronavirus disease (COVID-19), the side effects of this pandemic can still be devastating. Bullying and suicidality are significant global issues with detrimental effects on young people, particularly during school closure. This study aimed to identify the relationship between bullying and suicide risk among adolescents in Indonesia during the COVID-19 pandemic. Methods: A cross-sectional study was conducted on adolescents aged 14-18 years in May 2020 in Bandung, Indonesia, using a web-based closed survey. The Adolescent Peer Relations Instrument and the Suicide Behavior Questionnaire-Revised were used to measure bullying and risk of suicide. Multinomial logistic regression analysis was performed. Results: This study included 268 participants in 2020 and 175 participants in 2019. In 2020, the prevalence of perpetrators and victims of bullying combined was 74.6%. Meanwhile, in 2019, the prevalence of perpetrators and victims of bullying combined was 82.9%. Risk of suicide increased from 26.1% in 2019 (before the COVID-19 pandemic) to 36.5% in 2020 (during the first wave of the COVID-19 pandemic). The risk of perpetrators and suicide victims was higher than that of perpetrators and victims alone (odds ratio [OR]=4.0, 95% confidence interval [CI]=1.5-6.6 vs. OR=1.3, 95% CI=1.0-2.9 and OR=1.6, 95% CI=1.1-2.8, respectively). Conclusion: Bullying can enhance the likelihood of suicide among adolescents in Indonesia, and the risk was highest for the combination of victims and perpetrators. It is very important to provide early risk prediction for youths with bullying behavior and improve the knowledge and understanding of families and schools regarding the negative effects of bullying behavior.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.483-494
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• 2013

The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.

• Journal of Korean Academy of Nursing
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• v.13 no.1
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• pp.7-21
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• 1983

For the flexible and rational distribution of limited existing health resources based on measurements of individual risk, the socalled Risk Approach is being proposed by the World Health Organization as a managerial tool in maternal and child health care program. This approach, in principle, puts us under the necessity of developing a technique by which we will be able to measure the degree of risk or to discriminate the future outcomes of pregnancy on the basis of prior information obtainable at prenatal care delivery settings. Numerous recent studies have focussed on the identification of relevant risk factors as the Prior infer mation and on defining the adverse outcomes of pregnancy to be dicriminated, and also have tried on how to develope scoring system of risk factors for the quantitative assessment of the factors as the determinant of pregnancy outcomes. Once the scoring system is established the technique of classifying the patients into with normal and with adverse outcomes will be easily de veloped. The scoring system should be developed to meet the following four basic requirements. 1) Easy to construct 2) Easy to use 3) To be theoretically sound 4) To be valid In searching for a feasible methodology which will meet these requirements, the author has attempted to apply the“Likelihood Method”, one of the well known principles in statistical analysis, to develop such scoring system according to the process as follows. Step 1. Classify the patients into four groups: Group $A_1$: With adverse outcomes on fetal (neonatal) side only. Group $A_2$: With adverse outcomes on maternal side only. Group $A_3$: With adverse outcome on both maternal and fetal (neonatal) sides. Group B: With normal outcomes. Step 2. Construct the marginal tabulation on the distribution of risk factors for each group. Step 3. For the calculation of risk score, take logarithmic transformation of relative proport-ions of the distribution and round them off to integers. Step 4. Test the validity of the score chart. h total of 2, 282 maternity records registered during the period of January 1, 1982-December 31, 1982 at Ewha Womans University Hospital were used for this study and the“Questionnaire for Maternity Record for Prenatal and Intrapartum High Risk Screening”developed by the Korean Institute for Population and Health was used to rearrange the information on the records into an easy analytic form. The findings of the study are summarized as follows. 1) The risk score chart constructed on the basis of“Likelihood Method”ispresented in Table 4 in the main text. 2) From the analysis of the risk score chart it was observed that a total of 24 risk factors could be identified as having significant predicting power for the discrimination of pregnancy outcomes into four groups as defined above. They are: (1) age (2) marital status (3) age at first pregnancy (4) medical insurance (5) number of pregnancies (6) history of Cesarean sections (7). number of living child (8) history of premature infants (9) history of over weighted new born (10) history of congenital anomalies (11) history of multiple pregnancies (12) history of abnormal presentation (13) history of obstetric abnormalities (14) past illness (15) hemoglobin level (16) blood pressure (17) heart status (18) general appearance (19) edema status (20) result of abdominal examination (21) cervix status (22) pelvis status (23) chief complaints (24) Reasons for examination 3) The validity of the score chart turned out to be as follows: a) Sensitivity: Group $A_1$: 0.75 Group $A_2$: 0.78 Group $A_3$: 0.92 All combined : 0.85 b) Specificity : 0.68 4) The diagnosabilities of the“score chart”for a set of hypothetical prevalence of adverse outcomes were calculated as follows (the sensitivity“for all combined”was used). Hypothetidal Prevalence : 5％ 10％ 20％ 30％ 40％ 50％ 60％ Diagnosability : 12% 23% 40% 53% 64% 75% 80%.

• Journal of Global Scholars of Marketing Science
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• v.17 no.3
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• pp.1-19
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• 2007

1. Purpose Rural tourism is made by individuals with different characteristics, needs and wants. It is important to have information on the characteristics and preferences of the consumers of the different types of existing rural accommodation. The stud aims to identify the determinants of consumer preference by type of accommodations. 2. Methodology 2.1 Sample Data were collected from 1000 people by telephone survey with three-stage stratified random sampling in seven metropolitan areas in Korea. Respondents were chosen by sampling internal on telephone book published in 2006. We surveyed from four to ten-thirty 0'clock afternoon so as to systematic sampling considering respondents' life cycle. 2.2 Two-step cluster Analysis Our study is accomplished through the use of a two-step cluster method to classify the accommodation in a reduced number of groups, so that each group constitutes a type. This method had been suggested as appropriate in clustering large data sets with mixed attributes. The method is based on a distance measure that enables data with both continuous and categorical attributes to be clustered. This is derived from a probabilistic model in which the distance between two clusters in equivalent to the decrease in log-likelihood function as a result of merging. 2.3 Multinomial Logit Analysis The estimation of a Multionmial Logit model determines the characteristics of tourist who is most likely to opt for each type of accommodation. The Multinomial Logit model constitutes an appropriate framework to explore and explain choice process where the choice set consists of more than two alternatives. Due to its ease and quick estimation of parameters, the Multinomial Logit model has been used for many empirical studies of choice in tourism. 3. Findings The auto-clustering algorithm indicated that a five-cluster solution was the best model, because it minimized the BIC value and the change in them between adjacent numbers of clusters. The accommodation establishments can be classified into five types: Traditional House, Typical Farmhouse, Farmstay house for group Tour, Log Cabin for Family, and Log Cabin for Individuals. Group 1 (Traditional House) includes mainly the large accommodation establishments, i.e. those with ondoll style room providing meals and one shower room on family tourist, of original construction style house. Group 2 (Typical Farmhouse) encompasses accommodation establishments of Ondoll rooms and each bathroom providing meals. It includes, in other words, the tourist accommodations Known as "rural houses." Group 3 (Farmstay House for Group) has accommodation establishments of Ondoll rooms not providing meals and self cooking facilities, large room size over five persons. Group 4 (Log Cabin for Family) includes mainly the popular accommodation establishments, i.e. those with Ondoll style room with on shower room on family tourist, of western styled log house. While the accommodations in this group are not defined as regards type of construction, the group does include all the original Korean style construction, Finally, group 5 (Log Cabin for Individuals)includes those accommodations that are bedroom western styled wooden house with each bathroom. First Multinomial Logit model is estimated including all the explicative variables considered and taking accommodation group 2 as base alternative. The results show that the variables and the estimated values of the parameters for the model giving the probability of each of the five different types of accommodation available in rural tourism village in Korea, according to the socio-economic and trip related characteristics of the individuals. An initial observation of the analysis reveals that none of variables income, the number of journey, distance, and residential style of house is explicative in the choice of rural accommodation. The age and accompany variables are significant for accommodation establishment of group 1. The education and rural residential experience variables are significant for accommodation establishment of groups 4 and 5. The expenditure and marital status variables are significant for accommodation establishment of group 4. The gender and occupation variable are significant for accommodation establishment of group 3. The loyalty variable is significant for accommodation establishment of groups 3 and 4. The study indicates that significant differences exist among the individuals who choose each type of accommodation at a destination. From this investigation is evident that several profiles of tourists can be attracted by a rural destination according to the types of existing accommodations at this destination. Besides, the tourist profiles may be used as the basis for investment policy and promotion for each type of accommodation, making use in each case of the variables that indicate a greater likelihood of influencing the tourist choice of accommodation.