• Asian Oncology Nursing
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• v.11 no.2
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• pp.101-107
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• 2011

Purpose: This study was to understand and explain the adjustment of cancer patients receiving chemotherapy. Variables in this model were social support, uncertainty, resilience and coping. Methods: The data were collected for approximately five months from May through to September 2006 with cancer patients receiving chemotherapy or cancer patients finished chemotherapy using self-administered questionnaires. Descriptive statistics program, Amos 5.0, was employed to verify the goodness of the hypothetic model and the hypotheses formulated in the study. Results: The fitness of the finalized model turned out to be improved ($X^2$=332.644, df=70, GFI= .809, AGFI= .714, RMSEA= .138, AIC=402.644). Those variables made a 60.8% prediction of the patients adjustment. Conclusion: The hypothetic model presented in the study could serve to explain the adjustment of cancer patients receiving chemotherapy. Moreover, the use of the conceptual framework of the theory of planned nursing intervention would be very worthwhile as a strategy to boost the adjustment of cancer patients when receiving chemotherapy.

• The Korean Journal of Health Service Management
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• v.14 no.2
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• pp.1-14
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• 2020

Objectives : The purpose of this study was to test fitness of the structured model of SNS activities for health information. Methods : A structured questionnaire were administered to 500 subjects. A structural equation model was applied to collected data. Results : The response rate was 73.9%. The respondents mostly used Facebook and KakaoStory. They spent 70 minutes per day and 21~30% of this usage was taken by health information. In the variances, those who has religion more actively exchanged information about diseases and medical institutions. The goodness-of-fit of the model was .81(GFI) and .90(CFI). The main path was bridging capital -> bonding capital -> credibility -> SNS activities for health information. The path from quality of sharing information to SNS activities was not significant. It could be explained by the restriction of digital literacy. Conclusions : SNS activities for health information were determined by credibility, currency and bonding social capital. Bridging social capital, indirectly, influenced SNS activities through bonding social capital. Thus building bonding social capital would be a critical success factor for SNS.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.466-478
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• 2017

The purpose of this study was to examine the relationships between perceived parental attachment and the life satisfaction of college students, focusing on the mediating role of psychological basic needs(autonomy, competence and relatedness). The Participants of this study were 208 college students. The result was statistically treated using SPSS 21.0 program, Amos 21.0. Additionally, PROCESS Macro was used to verify the significant mediating effect. Results from structural equation modeling analyses indicated that a research model produced a better fit to the data than a alternative structural model. The final SEM model fit indices of $x^2$(df), CFI, TLI, RMSEA were met the acceptable criteria of model fitness. In other words, among the goodness-of-fit indexes of the final study model, $x^2=261.075$(p<.001), RMSEA is .082, TLI equals .925, CFI equals .940. The results showed the following: First, Parental attachment has a direct effect on autonomy, competence and relatedness. Also competence and relatedness have a direct effect on the life satisfaction. Second, Competence and relatedness showed a mediating effects on Parental attachment and the life satisfaction. The implications of these results were discussed and the further studies were suggested.

• Journal of Korean Academy of Nursing Administration
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• v.11 no.4
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• pp.371-384
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• 2005

Purpose: The aim of this study was to analysis path model of the research variables. Methods: The subjects of this study were 647 nurses who were working in the 8 general hospitals located in Seoul and Incheon area. The data were collected by self-reporting questionnaires. The data were analyzed using descriptive statistics and path analysis. Results: In the modified path model, overall fitness indexes were $X^2$= 223.27, goodness of fit index=0.90, root mean square residual=0.039, root mean square error of approximation=0.12, non-normed fit index=0.96, and normed fit index=0.90. From the model, among research variables that influence organizational effectiveness motivator, job satisfaction and organizational identification affected directly work performance. In internal marketing factors, paid-leave, communication and reward affected directly motivator. Motivator and hygiene factors affected directly job satisfaction, organizational commitment and organizational identification. Conclusion: With these findings, paid-leave, communication, reward, motivator, job satisfaction and organizational identification were direct or indirect predictors of the work performance. Therefore nursing managers ought to develop internal marketing strategies and motivation enhancing system for nurses based on this path model in order to improve the nursing organizational effectiveness.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.607-614
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• 2016

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of $polynomials{\times}3$ types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

• Journal of Korean Academy of Nursing
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• v.53 no.3
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• pp.295-308
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• 2023

Purpose: This study evaluated the validity and reliability of Shively and colleagues' self-efficacy for HIV disease management skills (HIV-SE) among Korean participants. Methods: The original HIV-SE questionnaire, comprising 34 items, was translated into Korean using a translation and back-translation process. To enhance clarity and eliminate redundancy, the author and expert committee engaged in multiple discussions and integrated two items with similar meanings into a single item. Further, four HIV nurse experts tested content validity. Survey data were collected from 227 individuals diagnosed with HIV from five Korean hospitals. Construct validity was verified through confirmatory factor analysis. Criterion validity was evaluated using Pearson's correlation coefficients with the new general self-efficacy scale. Internal consistency reliability and test-retest were examined for reliability. Results: The Korean version of HIV-SE (K-HIV-SE) comprises 33 items across six domains: "managing depression/mood," "managing medications," "managing symptoms," "communicating with a healthcare provider," "getting support/help," and "managing fatigue." The fitness of the modified model was acceptable (minimum value of the discrepancy function/degree of freedom = 2.49, root mean square error of approximation = .08, goodness-of-fit index = .76, adjusted goodness-of-fit index = .71, Tucker-Lewis index = .84, and comparative fit index = .86). The internal consistency reliability (Cronbach's α = .91) and test-retest reliability (intraclass correlation coefficient = .73) were good. The criterion validity of the K-HIV-SE was .59 (p < .001). Conclusion: This study suggests that the K-HIV-SE is useful for efficiently assessing self-efficacy for HIV disease management.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.450-456
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• 2017

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.

• Journal of Korean Society for Quality Management
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• v.47 no.1
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• pp.47-57
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• 2019

Purpose: This paper aims to investigate the concentration of fine particulate matter (PM2.5) in the Seoul area by predicting unhealthy days due to PM2.5 and comparing the regional differences. Methods: The extreme value theory is adopted to model and compare the PM2.5 concentration in each region, and each best model is selected through the goodness of fitness test. The maximum likelihood estimation technique is applied to estimate the parameters of each distribution, and the fitness of each model is measured by the mean absolute deviation. The selected model is used to estimate the number of unhealthy days (above $75{\mu}g/m^3$ PM2.5 concentrations) in each region, with which the actual number of unhealthy days are compared. In addition, the level of PM2.5 concentration in each region is analyzed by calculating the return levels for periods of 6 months, 1 year, 3 years, and 5 years. Results: The Mapo (MP) area revealed the most unhealthy days, followed by Gwanak (GW) and Yangcheon (YC). On the contrary, the number of unhealthy days was low in Seodaemun (SDM), Songpa (SP) and Gangbuk (GB) areas. The return level of PM2.5 was high in Gangnam (GN), Dongjak (DJ) and YC. It will be necessary to prepare for PM2.5 than other regions. On the contrary, Gangbuk (GB), Nowon (NW) and Seodaemun (SDM) showed relatively low return levels for PM2.5. However, in most of the regions of Seoul, PM25 is generated at a very poor level ($75{\mu}g/m^3$) every 6months period, and more than $100{\mu}g/m^3$ PM2.5 occur every 3 years period. Most areas in Seoul require more systematic management of PM2.5. Conclusion: In this paper, accurate prediction and analysis of high concentration of PM2.5 were attempted. The results of this research could provide the basis for the Seoul Metropolitan Government to establish policies for reducing PM2.5 and measuring its effects.

• Journal of muscle and joint health
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• v.5 no.1
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• pp.7-25
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• 1998

Rheumatoid arthritis, unlike other chronic diseases, causes the patients to experience uncertainty in their daily lives and thus to feel threat on their emotional comfort because of inconsistent and unpredictable symptoms such as pain. Therefore, a theoretical framework is needed for explanation of uncertainty in patients having rheumatoid arthritis. A hypothetical model was constructed on the basis of Mishel's Uncertainty Theory and other literature review. The model included 9 theoretical concepts and 19 paths. Subjects of the study constituted 330 partients who visited outpatient clinics of two university hospitals and one general hospital in Seoul. Self report questionnaires were used to measure the variables affecting uncertainty. Reliability coefficients of these instruments were found Cronbach's Alpha=$.70{\sim}.94$. In data analysis, SAS program and PC-LISREL 8.03 computer program were utilized for descriptive statistics and covariance structure analysis. The results of covariance structure analysis for model fitness were as follows : 1) Hypothetical model showed a good fit to the empirical data : Chi-square($X^2$)=41.81 (df=11, P=.000), Goodness of Fit Index=.974, Root Mean Square Residual=.049, Normed Fit Index=.928, Non Normed Fit Index=.814. 2) For the validity and the parcimony of model, a modified model was constructed by appending 2 paths and deleting 5 paths according to the criteria of statistical significance and meaningfulness. 3) The results of hypothesis testing were as follows : (1) Educational level, event familiarity and severity of illness had a direct effect on uncertainty : Event congruency had both direct and indirect effect on uncertainty : Credible authority and symptom consistency had a nonsignificant direct effect on uncertainty, (2) Illness duration, symptom consistency, and event congruency had a direct effect on severity of illness ; Credible authority had a both direct and indirect effect on severity of illness ; Event congruency had the greatest effect on severity of illness, and event familiarity had a nonsignificant direct effect on severity of illness.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.