• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.91-121
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• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Korean Journal of Social Welfare
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• v.58 no.4
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• pp.141-168
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• 2006

The present study explored and compared the risk factors of two subtypes of maritally violent men with those of a nonviolent comparison group. One type of batterers consisted of a community sample, and the other was sought from the criminal justice system. The identities of the male community batterers were not exposed to the society since their victims did not contact any of the social service agents related to domestic violence. To identify the different characteristics associated with two subtypes of woman abusers, a total of 152 nonviolent men, 82 male community batterers, and 336 offenders in a criminal justice system were considered. The results of the descriptive analysis showed that the level of physical violence of the community batterers was two times lower than that of the batterers who received legal punishments. The results of the multinominal logistic regression were as follows: (1) The variables that distinguished the male community batterers from the nonviolent men were the use of physical violence towards children, marital decision power, and income. (2) Four factors had been found to distinguish batterers in the criminal justice system from nonbatterers, namely: attitudes towards woman battering, education, violence towards children, and level of jealousy. (3) The community batterers showed a higher level of education and of stress as well as a longer period of marital relationship compared to the batterers in the criminal justice system. On the other hand, the batterers who received legal punishments had more severe alcohol problems and had an accepting attitude towards the use of violence. This study also investigated psychopathology among batterers using MCMI-III, based on 333 subjects. In terms of the mean scores, there were no subscales associated with personality pathology in all the male groups. Based on the logit model, the community batterers showed a stronger tendency towards having a passive-aggressive personality than did their counterparts, and they recorded a higher level of narcissism compared to the court-referred battering men. Post-traumatic stress was the only symptom that distinguished the batterers who received legal punishments from the other groups. The theoretical and practical implications of these results were pointed out and discussed in the paper.

• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.101-118
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• 2014

"Career maturity" is very crucial for high school students since it has an impact on their career path and decision-making. Not only that, it is also important in self-realization and happiness as well as maximizing human resources. When it comes to understanding high school students' career path, it is necessary to know how they perceive school life since they spend most of their time in school. It's also vital to observe in the perspective of students' personal growth. This study seeks to understand the relationship between "adaptation to school life" "self-esteem" and "career maturity". To accomplish this, the 7th additional surveys conducted by Welfare Panel Study were used. The survey was conducted among 496 high school students in order to come up with descriptive statistics and correlation between "adaptation to school life" and "self-esteem" as well as the level of "career maturity". Hierarchical multiple regression analysis was used to understand the effects of "adaptation to school life" and "self-esteem" on "career maturity." The Baron and Kennny mediation analysis were used to understand the effects when the mediating role of "self-esteem" comes into the relationship between "adaptation to school life" and "career maturity". The results of the analysis are as follows: First, the average age for high school students' "career maturity" is 2.07, while it is 1.91 for "self-esteem". For "adaptation to school life," the relationship between "obedience to school regulations" and "relationship with friends" was relatively higher than the relationship between "attitude toward school life" and "interest in school life" Second, high school students' "career maturity" "adaptation to school life" and "self-esteem" were thought to be statistically meaningful since it showed that they had a positive relationship with each other. Third, "interest in school life" "attitude toward school life" and "obedience to school life" and "relationship with friends" in which all of these are the sub factors for "adaptation to school life" together with "self-esteem" had an influence on high school students' "career maturity". Lastly, the relationship between "adaptation to school life" and "career maturity" was proved to be influenced by the partial mediating role of "self-esteem". As the study seeks to find relationships and the factors that affect high school students' "career maturity" meaningful information is given out for the development and progress of educational programs for "career maturity". This was done by understanding the fundamental and systematic approach towards "career maturity" in the students' perspective.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.66-79
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• 2016

A discussion has recently emerged over the increase of approvals of K-REITs, which is concluded on the basis of how to raise funds for business activity, fulfill the expected rate of return and maximize the management of managing investment funds. In addition, corporations need to acknowledge the necessity of the capital structure reflected in the current economic environment and decision-making processes. This research analyzed the characteristics by investment types and influence factors about the debt ratio of K-REITs. The data were collected from general management about business state, investment, and finance from 2002 to 2015 in K-REITs (except for the GFC period of 2007~2009). The results of the research demonstrated the high ratios of the largest shareholder characteristics, which are corporation, pension funds, mutual funds, banks, securities, insurance, and, recently, the increasing ratio of the largest shareholder and major stockholder. The investment of K-REITs is increasing the role of institutional investors that take a leading development of K-REITs. The behaviors of simultaneous investment of institutional investors were analyzed to show that they received higher interest rates than other financial institutions and ran in parallel with attraction and compensation. The results of the multiple regressions analysis, utilizing variables about debt ratio were as follows. The debt ratio showed a negative (-) relation that profitability is increasing, which matches the pecking order theory and trade off theory. On the other hand, investment opportunities (growth potential) showed a negative (-) relation and assets scale that indicated a positive (+) relation. The research results are reflected as follows. K-REITs focused on private equity REITs more than public offering REITs, and in the case of financing the capital of others, loan capital is operated under the guarantee of tangible assets (most of real estate) more than financing of the stock market. Further, after the GFC, the capital of others was actively utilized in K-REITs business, and the debt ratio showed that the determinant factors by the ratio and characteristics of the largest shareholder and investment products.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.149-163
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• 2010

Previous studies have shown that retail price promotion change consumers' purchase behavior and that retailers use price promotion more frequently. Keeping constant the benefits received by consumers, there are several ways for retailers to communicate a price promotion. For example, retailers can present a price reduction in absolute terms ($, ${\euro}$), percentage terms (%), or some combinations of these two methods (Della Bitta et al. 1981). Communicating a price promotion in different ways is similar to the framing of purchase decisions (Monroe 1990). Framing effects refers to the finding that subjects respond differently to different descriptions of the same decision question (Frisch 1993). Thus, the presentation of the promotion has an impact on consumer deal evaluation and hence retail sales. In fact, much research in marketing attests to the effects of price presentation on deal perception (Lichtenstein and Bearden 1989; Urbany et al. 1988; Yadav and Monroe 1993). In this sense, a number of marketing researches have argued that deal perceptions are also determined by the degree to which consumers are able to calculate the discounts and final purchase prices accurately (Estelami 2003a; Morwitz et al. 1998), which suggests that marketers may be able to enhance responses to discounts by improving calculation accuracy. Consequently, since calculation inaccuracies in the aggregate lead to the underestimation of discounts (Kim and Kramer 2006), consumers are more likely to appreciate a discounted offer following deeper processing of price information that enables them to evaluate a price discount more accurately. The purpose of this research is to examine the effect of different presentations of discount prices on consumer price perceptions. To be more precise, the purpose of this study is to investigate how different implementations of the same price promotion (semantic and visual salience) affect consumers' perceptions of the promotion and their purchase decisions. Specifically, the analysis will focus on the effect of price presentation on evaluation, purchase intentions and perception of savings. In order to verify the hypotheses proposed in the research, this paper will present an experimental analysis dealing with several discount presentations. In this sense, a2 (Numerical salience presentation: absolute and relative) x2 (Worded salience presentation: novel and traditional) x2 (Visual salience: red and blue) design was employed to investigate the effects of discount presentation on three dependent variables: evaluation, purchase intentions and perception of savings. Respondents were exposed to a hypothetical advertisement that they had to evaluate and were informed of the offer conditions. Once the sample finished evaluating the advertisement, they answered a questionnaire related to price salience and dependent dimensions. Then, manipulation checks were conducted to ensure that respondents remembered their treatment conditions. Next, a $2{\times}2{\times}2$ MANOVA and follow-up univariate tests were conducted to verify the research hypotheses suggested and to examine the effects of the individual factors (price salience) on evaluation, purchase intentions and perceived savings. The results of this research show that semantic and visual salience presentations have significant main effects and interactions on evaluation, purchase intentions and perception of savings. Significant numerical salience interactions affected evaluation and purchase intentions. Additionally, a significant worded salience main effect on perception of savings and interactions on evaluation and purchase intentions were found. Finally, visual salience interactions have significant effects on evaluation. The main findings of this research suggest practical implications that firms should consider when planning promotion-based discounts to attract consumer attention. Consequently, because price presentation has important effects on consumer perception, retailers should consider which effect is wanted in order to design an effective discount presentaion. Specifically, retailers should present discounts with a traditional style that facilitates final price calculation. It is thus important to investigate ways in which marketers can enhance the accuracy of consumers' mental arithmetic to improve responses to price discounts. This preliminary study on the effect of price presentation on consumer perception and purchase intentions opens the line of research for further research. The results obtained in this research may have been determined by a number of limiting conceptual and methodological factors. In this sense, the research deals with a variety of discount presentations as well as with their effects; however, the analysis could include additional salience dimensions and effects on consumers. Furthermore, a similar study could be carried out including a larger, more inclusive and heterogeneous sample of consumers. In addition, the experiment did not require sample individuals to actually buy the product, so it is advisable to compare the effects obtained in the research with real consumer behavior and perception.

• Journal of dental hygiene science
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• v.10 no.3
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• pp.185-190
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• 2010

This study was conducted to collect the basic data to provide for the purpose of the development of dental field. This research is to find the relationship between the level of recognized empowerment of hygienists working in dental clinics and the factors influencing on work achievement in the system of dental clinics. The population of the research is some randomely chosen dental clinics located in Seoul City during the period from March 22 to April 30, 2010, and the sample of 256 female dental hygienists working in the field were recruited as the analysis object group. The data on general characteristics and empowerment were collected by a questionnaire survey. The collected data were analyzed with the SPSS WIN 12.0 program. The following shows the results of this study. 1. The empowerment level of the analysis subject group was 3.63. It is significant statistically that the longer working career or higher annual salary, the higher empowerment level (p<0.05). 2. It shows the high level of empowerment in the group of hygienists who are in education counsel (p<0.01), and who have a permanent job (p<0.05) than others. 3. There is a significant correlation among capability, age, career, annual salary (p<0.01). 4. There is a significant correlation among self determination, age(p<0.05), career (p<0.01), annual salary (p<0.05). 5. In regression analysis, career is proved as one of the significant factors that is related to the empowerment in dental hygienists (p<0.05). This finding shows high level of empowerment in dental hygienists who hold high level of career, annual salary, education counsel jobs, or permanent jobs. Therefore, I suggest with the result of the study that the organization of dental clinic needs to improve its capability and efficiency with the efficient manpower management. Particularly, there need to be various kinds of leadership-trainning and education-program development to enhance empowerment in hygienists as professionals through independent self-decision making experiences and role do experiences.

• Tuberculosis and Respiratory Diseases
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• v.40 no.5
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• pp.532-539
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• 1993

Background: Physician's estimates of patient survival often influence clinical decisions, especially those near the end of life. In addition. clinical decisions frequently reflect trade-offs between morbidity and length of survival. As a result, accurate estimates of survival can be extremely useful in clinical decision. When the episode of acute respiratory failure in chronic obstructive lung disease, evaluation of the severity of the condition and short term prognosis is difficulit based on the available clinical or paraclinical data at the time of admission. Method: In this study, we performed a retrospective study in Chung Ang University Hospital, 74 patients (51 males, 23 females), who were hospitalized with chronic obstructive lung disease with acute respiratory failure from 1980 to 1992. We evaluated these patients to determine lung prognostic factors at time of admission in the Intensive Care Unit (ICU) that predict short term survival, and to determine the possible application of the Simplified Acute Phsiology Score (SAPS) to this population, All patients were treated with similar regimen during the hospitalization. Results: The results were as follows: 1) Hospital mortality was 34%(25/74 patients) and surival rate was 66%(49/74 patients) in COPD with acute respiratory failure. The prognosis of the older age was much poorer than those of the young age. 2) There was no difference in mortality according to the results of basal pulmonary test and arterial blood gas analysis. 3) The SAPS at admission was higher in those patients who expired(10.8) than the survived(6.5), and there was positive correlation between SAPS and mortality (r=0.91, p<0.05). 4) Prognostic factors in acute respiratory failure complicating COPD which were identifiable at time of admission to the ICU were as follows: cachexia, encephalopathy, serum creatinine and phosphate. Conclusion: In conclusion, the SAPS might have a good prognostic value for determination of short term survival among chronic obstructive lung disease with acute respiratory failure.

• Journal of Hospice and Palliative Care
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• v.9 no.1
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• pp.11-16
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• 2006

Purpose: This study is aimed to confirm nurse's attitudes and to investigate the factor analysis on organ donation in brain dead donors. Methods: This survey were collected from 198 nurses in three university hospitals and four general hospitals in B city with questionnaires developed by the author. The consent for this research was obtained from nursing managers, head nurses, and staff nurses after explaining the purpose of this research. Results: In questionnaires, 45 items about attitudes were included and positive and negative attitude were analyzed. The contents of factors are 'legal permission of brain death', 'one's will of organ donation at the brain death', 'need for educational program about brain dead during college curriculum', 'organ donation is good presents for others', 'connection with professional institutes', 'necessity of brain death', 'convenient to control of brain death' and 'the goods for organ transplantation in brain dead donors' as positive attitudes. Meanwhile, 'contrast to certain religion and dignity to life', 'negative dangers on brain dead permission', 'unbelief to the medical teams', 'burdens to ask organ donation to brain deads/families' and 'economical compensation' are factors as negative attitudes about organ donation in brain dead. The total mean point score of positive attitudes about organ donation in brain dead donors was $3.753{\pm}3.398$. The total mean point score of negative attitudes about organ donation in brain dead donors was $2.915{\pm}0.472$. Conclusion: The results of this study may be of help for the nurses who concern organ sharing and make effective interventions and educations to facilitate the decision making process for organ donation in brain dead donors or families.