• Journal of Hospice and Palliative Care
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• v.9 no.1
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• pp.17-29
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• 2006

Purpose: This research aimed to investigate the actual condition and satisfaction of volunteer activity in Australian hospital. Methods: Data was collected by self reported questionnaire from 101 volunteers and analyzed by frequency and percentage, t-test, ANOVA and Sheffe and Pearson's correlation coefficients using SPSS 12.0. Results: 1. Years involved in volunteer work were $5{\sim}10$ years (32.7%), above 10 years (30.7%), $2{\sim}3$ years (11.9%) and $3{\sim}5$ years (10.9%). Types of volunteer work were physical care (32.7%), physical and emotional care (14.9%), and others (18.8%). Types of allocation of tasks were by volunteer coordination (55.7%), and by volunteer preference and consent between volunteer and coordinator (both respectively, 20.5%). Main reasons for volunteer work were to help sick people (61.4%) and to make good use of leisure time (22.8%). Routes to start volunteer work were from his (her) own inquiries (43.4%), from hearing from other volunteers (30.7%) and from mass media (13.1%). 80.2% of volunteers had received some kinds of training or preparation for volunteer work. Suitability of volunteer's skill and ability to voluntary work were 'very well' (74.0%) and 'mostly well' (18.0%). Reimbursements or benefits received for volunteer work were token or lunch or group outing (31.7%), and token and lunch or group outing (19.8%). Evaluation frequency for volunteer work was occasionally (372%), frequently (30.9%), always (17.0%) and never (14.9%). Relationship with volunteer work coordinator was very good (85.0%). The relationship with other volunteers was very good (81.2%). The relationship with hospital staffs was very good (69.7%) and mostly good (21.2%). Family and friend's support for volunteer work was very good (83.2%). 2 The mean score of satisfaction for the hospital volunteer activity was $3.09{\pm}0.49\;(range:\;1{\sim}4)$. The highest score domain was 'social contact', $3.48{\pm}0.61$, and the lowest was 'social exchange', $1.65{\pm}0.63$. An item of the highest score was 'I have an opportunity to help other people' ($3.83{\pm}0.40$), and the lowest score item was 'I will receive compensation for volunteer work I have done ($1.10{\pm}0.78$).' 3. The satisfaction from hospital volunteer activity was shown by significant difference according to sex (t=2.038, P=0.044), marital status (F=3.806, P=0.013), years involved in volunteer work (F=3.326), nam reason to do volunteer work (F=2.707, P=0.035), receive any training or preparation for volunteer work (t=-1.982, 0=0.050), frequency of evaluation for volunteer work (F=7.877, P=0.000), suitability of volunteer's skill and ability to voluntary work (t=2.712, P=0.049), relationship with volunteer work coordinators (F=-2.517, P=0.013), relation with hospital staffs (F=5.202, P=0.007), and support of their volunteer work by their family and friends (t=-3.394, P=0.001). Conclusion: The satisfaction of hospice volunteer activity was moderate. The satisfaction for hospice volunteer activity was shown by significant difference according to sex (t=2.038, P=0.044), marital status (F=3.806, P=0.013), years involved in volunteer work (F=3.326), main reason to do volunteer work (F=2.707, P=0.035), receive any training or preparation for volunteer work (t=-1.982, 0=0.050), frequency of evaluation for volunteer work (F=7.877, P=0.000), suitability of volunteer's skill and ability to voluntary work (t=2.712, P=0.049), relationship with volunteer work coordinator (F=-2.517, P=0.013), relation with hospital staffs (F=5.202, P=0.007), and family and friend's support for volunteer work (t=-3.394, P=0.001). Therefore, it is necessary to consider various factors to improve the satisfaction of voluntary work.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.