• 한국호스피스완화의료학회:학술대회논문집
• /
• 2004.07a
• /
• pp.100-102
• /
• 2004

Under the current medical system, a terminal patient and his/her family who are neglected inevitably face various aspects of crises including not only physical, but also psychological, social, economic, spiritual and legal problems. Nurses often look after many terminal patents with these types of complicated problems. Therefore, educating the nurses who will take care of such patents would greatly reduce stress so the patents end could their lives in peace and without losing their dignity. This research is a quasi experimental study of nonequivalent control group. A pretest-posttest design where a basic education program is developed for nurses, who frequently treat terminal patents, to understand the importance of the role of hospice and to apply their understandings to treat terminal lancer patents. A sample of the nurses were taken from those who were working in general wards at two general hospitals in Seoul during October, 2003${\sim}$December 2003. The study was composed of 46 experimental group and 43 control group. A basic hospice education program was developed by taking emphasized and overlapping parts from advanced practice hospice nurses education course, short-term education course, an extensive literature survey and by consulting three professionals as well. With the group of 5 professors with vast experiences in oncolgy, 5 nursing administrator, 3 nursing practitioner, the tentative first version of the program was developed and reviewed. Afterwards, by utilizing person to person interviews with 2 head nurses experienced with terminal patients, 1 nurse in charge of hospice, 1 nurse on the contents of the program, and a person to person rating on the educating medium by a nurse were performed. The final version of a basic education program was developed after the second revision. The hospice basic education program consists of introduction to hospice, hospice and commucation, management of pain for terminal cancer patients, physical management for terminal cancer patients, socio-psycological caring of terminal cancer patients and management of death and separation. Total education time was four hours organized into 50 minutes of instruction and 10 minutes of break. $Powerpoint^{(R)}$ software was used as the education medium. As research tools, "Knowledge on Hospice" was developed by the author after receiving a review from one expert. "Attitude of Hospice Nursing" was revised Kim(2001)'s attitude measuring tool which was based on Wang(1998), Kwon(1989), Park and Sung(1991)'s tool. "Liability on nursing terminal patients" was used as developed by Zarits(1980) and Mongomory(1985) translated by Lee(1985). For collecting data, preliminary investigation prior to 1 week of the hospice basic education program and post-investigations after 1 week and 4 weeks of the education were carried out for the nurses at a general ward who understood and agreed on the purpose of the program. Collected data were analyzed throughout t-test, $x^2-test$, Manova test and Bonferroni correction in $SAS^{(R)}$ program. The summary of the investigation is as follows: Hypothesis 1: "Educated experimental group would possess more knowledge on hospice compared to the un-educated control group" was supported after 1 (F=12.14, p=.00) and 4 (F=5.3, p=.02) weeks of education. Hypothesis 2: "Educated experimental group would take a positive attitude toward hospice nursing compared to the un-educated control group" was supported after 1(F=3.92, p=.05) and 4(F=5.05, p=.02) weeks of education. Hypothesis 3: "Educated experimental poop would feel less liability compared to the un-educated control group in nursing terminal cancer patients' was rejected. In this study, it was found that knowledge on hospice was significantly important. By applying hospice basic education programs to nurses, the education program helped nurses to take a positive attitude toward terminal patients. It was, however, seen that the education program had no effect on alleviating liability in nursing terminal patients. Therefore, it is expected that this educational program would help hospices and nurses at general wards to understand the concept and the role of hospice so that terminal patents, now neglected under current medical system, would be able to end their lives in peace.

• Journal of vocational education research
• /
• v.30 no.4
• /
• pp.23-49
• /
• 2011

Faculty performance evaluation system has been settled down as an uncomfortable but unavoidable system, and it is one of the most important factors to grow the college competitiveness up. In this study, we selected and surveyed faculty evaluation models of several universities and colleges in Korea, and analyzed by comparing each evaluation areas of educational achievement, college-industry collaboration, research, and service. We also identified the properties of the current faculty evaluation models of the junior colleges, and derived several problems from these models such as an imitation of four-year university model, a disorders of job evaluation with respect to the attributes of classified jobs, a large variation of individual item weights, and an insufficient reflection of major characteristics. Based on these surveys and analysis, an improved faculty evaluation model for the junior college is proposed in this study. This model proposed four basic areas-educational achievement, college-industry collaboration, research, and service by considering the importance of the college-industry collaboration in the junior college-as well as the team evaluation area. Weights of the SCI-class paper was selected as a criterion for the arrangement of objective comparison of each evaluation items. We showed the integration method of several different evaluation model with respect to the attributes of classified jobs of each faculties, and evaluation plan of variational characteristics according to the majors of individuals in this model. Finally, we introduced an area fail and rating system to operate efficiently the proposed faculty evaluation model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.5
• /
• pp.77-90
• /
• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.173-198
• /
• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.