Norm-based comparison of SF -12 physical and mental health scale scores for a sample of employees of Broward County

In the new health paradigm, the connotation of health has extended beyond the measures of morbidity and mortality to include wellness and quality of life. Comprehensive assessments of health go beyond traditional biological indicators to include measures of physical and mental health status, social role-functioning, and general health perceptions. To meet these challenges, tools for assessment and outcome evaluation are being designed to collect information about functioning and well-being from the individual's point of view.^ The purpose of this study was to profile the physical and mental health status of a sample of county government employees against U.S. population norms. A second purpose of the study was to determine if significant relationships existed between respondent characteristics and personal health practices, lifestyle and other health how the tools and methods used in this investigation can be used to guide program development and facilitate monitoring of health promotion initiatives.^ The SF-12 Health Survey (Ware, Kosinski, & Keller, 1995), a validated measure of health status, was administered to a convenience sample of 450 employees attending one of nine health fairs at an urban worksite. The instrument has been utilized nationally which enabled a comparative analysis of findings of this study with national results.^ Results from this study demonstrated that several respondent characteristics and personal health practices were associated with a greater percentage of physical and/or mental scale scores that were significantly "worse" or significantly "better" than the general population. Respondent characteristics that were significantly related to the SF-12 physical and/or mental health scale scores were gender, age, education, ethnicity, and income status. Personal health practices that were significantly related to SF-12 physical and/or mental scale scores were frequency of vigorous exercise, presence of chronic illness, being at one's prescribed height and weight, eating breakfast, smoking and drinking status. This study provides an illustration of the methods used to analyze and interpret SF-12 Health Survey data, using norm-based interpretation guidelines which are useful for purposes of program development and collecting information on health at the community level. ^

Background traffic modeling for large-scale network simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Exploring therapy process and outcome in interventions that target at risk adolescents

The purpose of this study was to conduct a larger scale replication and extension study on the use of a Session Impact Measure the Session Evaluation Form. Ninety-one public high school students in Miami Florida were obtained through self or counselor referrals and placed in one or two of five counseling groups for one or two school semesters. To investigate differences in therapy processes across counseling groups, participants were administered a Session Evaluation Form at the end of each therapy session. This assessed group members' perception of four therapy process domains, Group, Facilitator, Skills and Exploration Impacts. The pattern significant results for the MANOVAs provided strong evidence for the greater impact of the group on therapy process relative to the impact of facilitator. Further research is needed to identify more specifically, ways, group process differences interact with other treatment variables.

Background Traffic Modeling for Large-Scale Network Simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.