• Human Ecology Research
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• 제59권2호
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• pp.185-197
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• 2021

This study examined the impact of job demand and organizational culture on new task difficulties, burnout, and job satisfaction using a survey data of 145 family specialists in Healthy Family and Multicultural Family Support during the COVID-19 pandemic. We used the job demand-resources model and the competing values model to categorize the four dimensions of organizational culture as a conceptual framework for this study. We found that the mean of work overload was higher than the means of job conflict and job ambiguity. Our latent profile analysis proposed four profiles of organizational culture: cultural absence type, authoritative culture type, middle cultural balance type, and high cultural balance type. The results of multiple regression analyses showed that work overload was positively associated with difficulties in new task performance and burnout, job ambiguity was positively related to burnout, and job conflict and ambiguity were negatively related to job satisfaction. These findings imply that the higher the job demands reported by family specialists, the higher the level of burnout and the lower the job satisfaction. In addition, organizational culture was a unique predictor of burnout and lower level of job satisfaction. Family specialists in the groups with a high cultural balance were Family specialists in the groups with a high cultural balance were more likely to have lower levels of burnout than those in the culture absence and in the middle culture balance, and higher job satisfaction than the other groups. The results suggest that management strategies to build a creative workplace culture can prevent burnout and improve job satisfaction.

• 한국콘텐츠학회논문지
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• 제22권9호
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• pp.473-484
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• 2022

본 연구는 노년기 주관적 건강상태와 우울 간의 종단적 상호인과관계를 밝히는 데 있다. 이를 위해 전국단위의 조사자료인 고령화연구패널조사 제5차연도 자료부터, 제6차연도, 제7차연도 자료까지 추적 조사에 모두 응답한 65세 이상 3,363명을 대상으로 자기회귀교차지연모형을 검증하였다. 주요 분석결과는 다음과 같다. 첫째, 이전 시점의 주관적 건강상태는 이후 시점의 주관적 건강상태에 정(+)적으로 유의한 영향을 미쳤다. 둘째, 이전 시점의 우울은 이후 시점의 우울에 정(+)적으로 유의한 영향을 미치는 것으로 나타났다. 셋째, 조사대상 기간 동안 이전 시점의 주관적 건강상태는 이후 시점의 우울에 부(-)적으로 유의한 영향을 미치는 것으로 나타났으나, 그러나 이전 시점의 우울은 이후 시점의 주관적 건강상태에 유의미한 영향을 미치지 않았다. 연구결과에 기초해 노년기 초기 기초체력 증진을 위한 체계적인 정책의 필요성과 건강증진 프로그램의 확대 시행 및 통합적 우울 관리 프로그램 시행이 필요함을 제안하였다.

• Steel and Composite Structures
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• 제48권2호
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• 지질공학
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• 제34권1호
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• 간호행정학회지
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• 제4권1호
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• pp.193-214
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• 1998

The purpose of the present study was to examine the causal model of nurses' organizational commitment. Based on literature review and Fishbein's behavioral intentions model ((Fishbein. 1967: Fishbein & Ajzen. 1975). the organizational commitment was conceptualized within a motivational framework that mediate between antecedents variables and outcome variables. Antecedent variables were pay, promotional chances. continuing education opportunity. rigidity of the administration. paticipative decision making, latitude, group support, role conflict, work load, need for achievement. experience and pride for professional nursing. Outcome variable was turnover intention. The subjects were 373 nurses who were working at 2 large general hospitals located in Seoul. It represents a response rate of 94%. Data for this study was collected from August 29 to September 22 in 1997 by Questionnaire. Path analysis with LISREL 7.16 prigram was used to test the fit of the proposed conceptual model to data and to examine the causal relationships among variables. The result showed that both the proposed model and the modified model fit the data excellently. It needs to be notified, however. that path analysis can not count measurment errors: measurement error can attenuate estimates of coefficient and explanatory power. Nontheless the model revealed considerable explanatory power for organizational commitment (58%), pride for professional nursing (50%) and turnover intention(40%). In predicting nurses' organizational commitment, the findings of this study clearly demonstrated 'the pride for professional nursing' might be the most important variables of all the antecedent variables. Group support, role conflict, need for achievement were also found to be important determinants for the organizational commitment and turnover intention, The result showed experience might be a predictor for 'pride for professional nursing' and 'turnover intention' but not 'organizational commitment', 'Rigidity of the administration' and latitude were also found to have important roles in predictingr the organizational commitment, while participative decision making might have an impact on turnover intention. On the other hand promotional chance had an influence on all the outcome variables, while pay only on turnover intention. In predicting turnover intention, the result clearly revealed 'the pride for professional nursing' and 'organizational commitment' might be the most powerful predictors among all the variables. Theses results were discussed, including directions for the future research and practical implications drawn from the research were suggested.

• 한국지구과학회지
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• 제28권2호
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• pp.214-226
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• 2007

강수는 다양한 대기 변수들의 영향으로 나타나기 때문에 비선형성이 매우 강하다. 따라서 역학 모형을 통해 예측된 강수의 보정은 비선형 모형인 인공 신경망 등을 통해 가능할 것이지만, 인공 신경망의 경우 초기 가중치 선택, 지역 최소화 문제, 뉴런의 수 결정 등의 문제로 인한 한계가 있다. 그러므로 본 연구에서는 가장 보편적으로 사용되는 다중 선형 회귀 모형을 이용하여 CGCM에 의해 모사된 강수를 보정하였으며, 예측성을 살펴보았다. 이를 위하여 우선 PNU/CME 접합 대순환 모형(Coupled General Circulation model, CGCM)(박혜선과 안중배, 2004)을 이용하여 1979년부터 2005년까지 매해 4월부터 8월까지 5개월간 앙상블 적분을 하였다. 적분 결과 중 한반도를 포함한 동북아시아 지역$(110^{\circ}E-145^{\circ}E,\;25^{\circ}N-55^{\circ}N)$의 여름철인 6월(리드 2), 7월(리드 3), 8월(리드 4) 및 여름철 평균인 JJA(from June to August) 기간의 PNU/CME CGCM에 의해 모사된 강수를 보정하기 위해 다중 선형 회귀(Multiple Linear Regression, MLR)를 이용하였다. PNU/CME 접합 대순환 모형의 결과 중 강수, 500 hPa 연직 속도, 200 hPa 발산장, 지상 기온 등의 예측 인자와 관측 강수와의 선형적인 관계를 이용하여 MLR 모형을 구축하였다. 그리고 교차 검증(cross- validation)을 수행하여 PNU/CME 접합 대순환 모형의 결과와 교차 검증 결과를 비교하였다. 상관계수, 적중률 (hit rate), 오보율(false alarm rate) 그리고 Heidke 기술 점수(Heidke skill score) 등을 살펴본 바, 보정하지 않은 모형의 결과에 비해 MLR 모형을 이용하여 보정한 결과의 강수에 대한 예측성이 뛰어난 것을 알 수 있었다.

• 한국지리정보학회지
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• 제11권2호
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• pp.132-147
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• 2008

유비쿼터스 서비스(u-서비스) 우선순위 평가에 대한 선행연구는 공급자에 주안점을 두고 있어 결과의 타당성에서 한계를 노출하고 있다. 특히 지금까지 중앙부처 및 지방자치단체에서 제시하고 있는 대부분의 유비쿼터스 추진사업은 u-서비스 사업 우선순위가 정책담당자의 경험이나 직관, 또는 소수전문가들의 경험적 지식 등에 의존하여 선정되고 있다. 이러한 한계를 극복하기위해 u-서비스 공급자 측면과 수요자 측면의 요구를 모두 충족할 수 있는 u-서비스 우선순위 평가모형을 개발하고자 본 연구가 수행되었다. u-서비스 우선순위 모형개발을 위해 선행 연구를 토대로 수요자와 공급자의 서비스 결정요인을 탐색적으로 도출하고, 전문가와의 브레인스토밍을 통해 u-서비스 결정의 확정적 요인을 추출하였다. 도출된 요인들을 계층분석(AHP)모형으로 설정하고 전문가 설문조사를 통해 요인별 가중치와 우선순위를 도출하여 u-지역정보화에서 제시한 대구지역의 특화서비스를 평가하였다. 평가 결과 대구지역은 지역물류지원서비스, 시각장애인 길안내 서비스, u-텔레매틱스 서비스가 상위권을 차지한 반면, u-지역정보화에서 제시한 안전방재지원서비스와 산업특화거리 등의 서비스는 중위권을 차지하고 있어 공급자 위주의 u-서비스 선정결과와는 차별성을 보였다.

• 한국임상수의학회지
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• 제33권2호
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• 문화기술의 융합
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• 제10권4호
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• pp.159-168
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• 2024

본 연구는 COVID-19로 이후 임상실습을 경험한 간호대학생을 대상으로 임상수행능력에 미치는 영향요인을 파악하여 임상수행능력 향상을 위한 기초자료제공을 위해 수행되었다. 본 연구는 G도 소재의 간호학과 2곳의 재학생 144명을 대상으로 2023년 10월 10일부터 10월 27일까지 자료수집하였다. 수집된 자료는 서술통계와 차이분석(t-est, on-way ANOVA), 상관관계, 위계적 회귀분석으로 분석하였다. 연구결과 모델 1에서는 성격유형 중 외향적과 혼합적이 임상수행능력을 설명하는 유의한 예측요인으로 나타났다. 모델 1의 적합도는 통계적으로 유의하였고, 설명력은 9.2%였다(F=8.256, p<.001). 모델 2에서는 대인관계능력과 간호술기자신감이 임상수행능력을 설명하는 유의한 예측요인으로 나타났다. 간호술기자신감이 임상수행능력을 가장 잘 예측하는 요인이었고, 그 다음으로 대인관계능력이었다. 모델의 설명력은 50.1%였고, 모델 1에 비해 41.3% 증가하였다. 모델적합도도 통계적으로 유의하였다. 직접간호 수행의 기회를 높이고 간호대학생의 간호역량을 향상시키기 위해 다양한 상황을 재현하는 시뮬레이션 교육이 강화되어야 한다. 시뮬레이션교육을 통해 간호술기자신감과 대인관계능력을 향상시키면 결국 임상수행능력이 향상되고 이는 간호사로 취업되었을 때 간호역량으로 발휘될 수 있을 것이다.

• 대한인간공학회지
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• 제36권3호
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• pp.183-196
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• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.