Volunteering: Demographic characteristics, motives and potential benefits from service

Volunteering is intricately woven into the fabric of our society. In 2009 alone, approximately 63.4 million Americans participated in volunteer activities, collectively donating over 8.1 billion service-hours (Corporation for National and Community Service [CNCS], 2010). Each service-hour is determined by the U.S. Bureau of Labor Statistics (2010) to be valued at $20.85/hr which translates to a national savings of $169 billion. Thus, we can clearly observe the significance of volunteer contribution to the overall benefit of society. In addition, there is now evidence that voluntary service may also benefit the actual volunteer, especially individuals who are 65+ years. As we reach 2020 this elderly class, composed of nearly 13 million (CNCS, 2010) Americans, will be of much consequence. Their potential to contribute in community-related efforts may save the U.S. billions in labor costs, and may also help reduce healthcare-related expenditures if volunteering proves to be a protective factor. In this literature review, we set out to explore the potential relationship between volunteer participation and increased mental and physical wellness. We also examined volunteer demographic characteristics and common motives for engaging in service-related activities. Analysis showed that volunteer work often combined low-impact physical activity and mental satisfaction from serving others, resulting in overall health benefit. Demographic characteristics displayed were consistent with previous studies and found that a majority of volunteers were female, White, married status, having received college degree or higher, employed, middle-high SES. In addition, age was seen to be a key characteristic in forecasting volunteer motivation and self-reported perceived health benefits.^

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

A MANAGEMENT INFORMATION SYSTEM AND EVALUATION SCHEME FOR THE UNIVERSITY OF TEXAS EMPLOYEE ASSISTANCE PROGRAMS

Early Employee Assistance Programs (EAPs) had their origin in humanitarian motives, and there was little concern for their cost/benefit ratios; however, as some programs began accumulating data and analyzing it over time, even with single variables such as absenteeism, it became apparent that the humanitarian reasons for a program could be reinforced by cost savings particularly when the existence of the program was subject to justification.^ Today there is general agreement that cost/benefit analyses of EAPs are desirable, but the specific models for such analyses, particularly those making use of sophisticated but simple computer based data management systems, are few.^ The purpose of this research and development project was to develop a method, a design, and a prototype for gathering managing and presenting information about EAPS. This scheme provides information retrieval and analyses relevant to such aspects of EAP operations as: (1) EAP personnel activities, (2) Supervisory training effectiveness, (3) Client population demographics, (4) Assessment and Referral Effectiveness, (5) Treatment network efficacy, (6) Economic worth of the EAP.^ This scheme has been implemented and made operational at The University of Texas Employee Assistance Programs for more than three years.^ Application of the scheme in the various programs has defined certain variables which remained necessary in all programs. Depending on the degree of aggressiveness for data acquisition maintained by program personnel, other program specific variables are also defined. ^