The conflict resolution behaviors of occupational therapy students as a function of the personality types, demographic characteristics and contextual factors

Interpersonal conflicts have the potential for detrimental consequences if not managed successfully. Understanding the factors that contribute to conflict resolution has implications for interpersonal relationships and the workplace. Researchers have suggested that personality plays an important and predictable role in conflict resolution behaviors (Chanin & Schneer, 1984; Kilmann & Thomas, 1975; Mills, Robey & Smith, 1985). However, other investigators have contended that contextual factors are important contributors in triggering the behavioral responses (Shoda & Mischel, 2000; Mischel & Shoda, 1995). The purpose of this study was to investigate the relationships among personality types, demographic characteristics and contextual factors on the conflict resolution behaviors reported by graduate occupational therapy students (n = 125). ^ The study design was correlational. The Myers Briggs Type Indicator (MBTI) and the Thomas-Kilmann (MODE) Instrument were used to establish the personality types and the context independent conflict resolution behaviors respectively. The effects of contextual factors of task vs. relationship and power were measured with the Conflict Case Scenarios Questionnaire (CCSQ). One-way ANOVA and linear regression procedures were used to test the relationships between personality types and demographic characteristics with the context independent conflict behaviors. Chi-Square procedures of the personality types by contextual conditions ascertained the effects of contexts in modifying the resolution modes. Descriptive statistics established a profile of the sample. ^ The results of the hypotheses tests revealed significant relationships between the personality types of feeling-thinking and sensing-intuition with the conflict resolution behaviors. The contextual attributes of task vs. relationship orientation and of peer vs. supervisor relationships were shown to modify the conflict behaviors. Furthermore, demographic characteristics of age, gender, GPA and educational background were shown to have an effect on the conflict resolution behaviors. The knowledge gained has implications for students' training, specifically understanding their styles and use of effective conflict resolution strategies. It also contributes to the knowledge on management approaches and interpersonal competencies and how this might facilitate the students' transition to the clinical role. ^

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Optimum wireless power transmission for sensors embedded in concrete

Various nondestructive testing (NDT) technologies for construction and performance monitoring have been studied for decades. Recently, the rapid evolution of wireless sensor network (WSN) technologies has enabled the development of sensors that can be embedded in concrete to monitor the structural health of infrastructure. Such sensors can be buried inside concrete and they can collect and report valuable volumetric data related to the health of a structure during and/or after construction. Wireless embedded sensors monitoring system is also a promising solution for decreasing the high installation and maintenance cost of the conventional wire based monitoring systems. Wireless monitoring sensors need to operate for long time. However, sensor batteries have finite life-time. Therefore, in order to enable long operational life of wireless sensors, novel wireless powering methods, which can charge the sensors’ rechargeable batteries wirelessly, need to be developed. The optimization of RF wireless powering of sensors embedded in concrete is studied here. First, our analytical results focus on calculating the transmission loss and propagation loss of electromagnetic waves penetrating into plain concrete at different humidity conditions for various frequencies. This analysis specifically leads to the identification of an optimum frequency range within 20–80 MHz that is validated through full-wave electromagnetic simulations. Second, the effects of various reinforced bar configurations on the efficiency of wireless powering are investigated. Specifically, effects of the following factors are studied: rebar types, rebar period, rebar radius, depth inside concrete, and offset placement. This analysis leads to the identification of the 902–928 MHz ISM band as the optimum power transmission frequency range for sensors embedded in reinforced concrete, since antennas working in this band are less sensitive to the effects of varying humidity as well as rebar configurations. Finally, optimized rectennas are designed for receiving and/or harvesting power in order to charge the rechargeable batteries of the embedded sensors. Such optimized wireless powering systems exhibit significantly larger efficiencies than the efficiencies of conventional RF wireless powering systems for sensors embedded in plain or reinforced concrete.

Gender differences in the influence of protective factors, risk factors, and health risk behaviors on HIV risk behaviors among youth in South Florida

Adolescents engage in a range of risk behaviors during their transition from childhood to adulthood. Identifying and understanding interpersonal and socio-environmental factors that may influence risk-taking is imperative in order to meet the Healthy People 2020 goals of reducing the incidence of unintended pregnancies, HIV, and other sexually transmitted infections among youth. The purpose of this study was to investigate gender differences in the predictors of HIV risk behaviors among South Florida youth. More specifically, this study examined how protective factors, risk factors, and health risk behaviors, derived from a guiding framework using the Theory of Problem Behavior and Theory of Gender and Power, were associated with HIV risk behavior. A secondary analysis of 2009 Youth Risk Behavior Survey data sets from Miami-Dade, Broward, and Palm Beach school districts tested hypotheses for factors associated with HIV risk behaviors. The sample consisted of 5,869 high school students (mean age 16.1 years), with 69% identifying as Black or Hispanic. Logistic regression analyses revealed gender differences in the predictors of HIV risk behavior. An increase in the health risk behaviors was related to an increase in the odds that a student would engage in HIV risk behavior. An increase in risk factors was also found to significantly predict an increase in the odds of HIV risk behavior, but only in females. Also, the probability of participation in HIV risk behavior increased with grade level. Post-hoc analyses identified recent sexual activity (past 3 months) as the strongest predictor of condom nonuse and having four or more sexual partners for both genders. The strongest predictors of having sex under the influence of drugs/alcohol were alcohol use in both genders, marijuana use in females, and physical fighting in males. Gender differences in the predictors of unprotected sex, multiple sexual partners, and having sex under the influence were also found. Additional studies are warranted to understand the gender differences in predictors of HIV risk behavior among youth in order to better inform prevention programming and policy, as well as meet the national Healthy People 2020 goals.

An examination of the possible consolidation of African American political power through selective migration

This study examined whether African Americans could consolidate political power through a migration into a specific state or set of states in sufficient numbers to establish a voting majority within that state. In order to examine the feasibility and efficacy of this strategy, a variety of factors were reviewed, including the historical context leading to the current situation; the political benefits to be derived from the control of a state; and the population and migration patterns of African Americans. The results indicated that this strategy could succeed in providing significant substantive and symbolic political benefits to the African American community, but could also have some negative repercussions.

Gender Differences in the Influence of Protective Factors, Risk Factors, and Health Risk Behaviors on HIV Risk Behaviors Among Youth in South Florida

Adolescents engage in a range of risk behaviors during their transition from childhood to adulthood. Identifying and understanding interpersonal and socio-environmental factors that may influence risk-taking is imperative in order to meet the Healthy People 2020 goals of reducing the incidence of unintended pregnancies, HIV, and other sexually transmitted infections among youth. The purpose of this study was to investigate gender differences in the predictors of HIV risk behaviors among South Florida youth. More specifically, this study examined how protective factors, risk factors, and health risk behaviors, derived from a guiding framework using the Theory of Problem Behavior and Theory of Gender and Power, were associated with HIV risk behavior. A secondary analysis of 2009 Youth Risk Behavior Survey data sets from Miami-Dade, Broward, and Palm Beach school districts tested hypotheses for factors associated with HIV risk behaviors. The sample consisted of 5,869 high school students (mean age 16.1 years), with 69% identifying as Black or Hispanic. Logistic regression analyses revealed gender differences in the predictors of HIV risk behavior. An increase in the health risk behaviors was related to an increase in the odds that a student would engage in HIV risk behavior. An increase in risk factors was also found to significantly predict an increase in the odds of HIV risk behavior, but only in females. Also, the probability of participation in HIV risk behavior increased with grade level. Post-hoc analyses identified recent sexual activity (past 3 months) as the strongest predictor of condom nonuse and having four or more sexual partners for both genders. The strongest predictors of having sex under the influence of drugs/alcohol were alcohol use in both genders, marijuana use in females, and physical fighting in males. Gender differences in the predictors of unprotected sex, multiple sexual partners, and having sex under the influence were also found. Additional studies are warranted to understand the gender differences in predictors of HIV risk behavior among youth in order to better inform prevention programming and policy, as well as meet the national Healthy People 2020 goals.