This study examines portrayals of adolescent girls on Music Television (MTV). A content analysis of 26 hours of MTV programming was conducted and analyzed using quantitative and qualitative methods. Analyzed programming was shown August-November 2001. Dates were chosen randomly; times were chosen randomly from the pools of hours in which adolescents usually watch television. Adolescent girls predominantly appear in the background of MTV programs. Many of them cheer for male celebrities, but only 12% speak. The content analysis reveals that a narrow beauty ideal is promoted on the channel-most girls are thin, White, and wearing revealing clothing. It is argued that MTV portrayals exacerbate girls' body dissatisfaction, sexual objectification, and confidence slide. This study is significant because the stories told on MTV are reflected in the lived world, and those stories suggest that girls should sit quietly in the background and be thin and White to be considered beautiful.

The current study examined the role of a novel, adapted dialogic reading curriculum and its impact on preschoolers with autism and their interactions with their parents during shared book reading. The aim of this study was to determine the impact of the curriculum on the effects of child social reciprocity and parents’ feelings of competence and confidence when trained on implementation strategies. Pre- and post-test measures were conducted for four parent-child dyads to measure the impact of adapted dialogic reading on child social reciprocity and parents’ feelings of competence and confidence. Although the sample size was small, clear trends were seen suggesting adapted dialogic reading methods may result in greater increases in social reciprocity behaviors such as contingent responses to questions and joint attention during shared book reading. Positive trends also suggest that when parents are trained to implement adapted dialogic reading strategies, their feelings of competence and confidence are increased.

Hundreds of state regulations were passed during the “managed care backlash” of the late 1990s and early 2000s. Many of these anti-managed care regulations eased or eliminated constraints on patient utilization of health care services imposed by managed care organizations. Other regulations gave managed care providers more flexibility in the way they practiced care or helped patients appeal denials of claims. Despite the effort undertaken to pass these regulations, limited research exists on whether the regulations achieved their goal. To fill this gap, this study takes advantage of the variety of regulations enacted during the managed care backlash of the late 1990s and early 2000s to investigate their impact on patient-reported quality of care and mortality for managed care enrollees. The results indicate the regulations did improve patient-reported outcomes, but to varying degrees and only in the latter period of the backlash. Specifically, managed care enrollees who lived in states that adopted moderate-intensity regulations between 2000 and 2004 reported relatively better improvements in access to care and confidence in their provider than did managed care enrollees in states with low-intensity backlash regulations. The positive effect on access to care was similar in states that adopted high-intensity regulations. However, no positive effect was found for any outcome in the first period (1996-2000). These results show that states with the most intense regulatory backlash did not realize better patient-reported outcomes. Instead, states that pursued moderate-intensity backlash regulations experienced relatively better outcomes for their managed care enrollees.

Thousands of service persons and veterans may be leaving military service annually without required skills and not receiving timely career counseling and interventions needed to aid in their career transitions. Knowledge about service persons' career adaptability, confidence, and readiness could enhance the actions of all stakeholders to address the challenges that accompany career transitions and may aid in identifying needed counseling and interventions. Using a survey containing the Career Transitions Inventory and the Career Futures Inventory-Revised, perspectives were obtained from service persons (N = 264) while attending Transition Assistance Program workshops. Two research questions for the study examined associations between individuals' career adaptability and 2 transition variables: confidence and readiness. Statistical testing was accomplished using Pearson correlation coefficient, t test, and 1-way analysis of variance. Correlations of transition confidence and overall career adaptability scores indicated a low negative correlation (r (262) = -0.4299, p < .01), and correlations of transition readiness and overall career adaptability scores indicated a low positive correlation (r (262) = 0.3988, p < .01). In addition, significant differences were noted when examining survey results based on demographic variables such as race, education, marital status, highest pay-grade achieved, and years of service. This study contributes to social change by demonstrating techniques for assessing personal traits. Implications are discussed for using self-reported data for counseling and interventions for individuals, which could enhance their career transition experiences.

Increasing global occurrences of highly infectious, easily transmissible diseases unfamiliar to nurses affect the learning environment and the required skill set for professional nurses. The global threat of Ebola Virus Disease and other high-risk diseases requiring biocontainment necessitates competency in the management of complex patient needs, while ensuring safety measures that prevent spread of the potentially fatal disease. Guided by Bandura's social cognitive theory, this quantitative correlational study addressed the relationships between nurses' professional characteristics and their perceived self-efficacy when providing care to highly infectious patients requiring biocontainment. A full census of 92 nurses was used to recruit participants from eligible nurses for this study. Participants anonymously completed a cross-sectional electronic survey consisting of the Nursing Care Self-Efficacy Scale (NCSES) and questions related to the nurses' professional practice characteristics. Data analysis included descriptive statistics, correlations, and multiple linear regression. Results showed that the number of biocontainment drills and a higher level of formal education were significantly correlated with a higher total NCSES score. Years of nursing significantly predicted a higher total NCSES score. Results support the establishment of prerequisites criteria for learner participation in biocontainment training and the inclusion of multiple drill within the education design. Findings from this study may inform positive social change through educational enhancements that support the development of professional self-efficacy and competency in skill performance for nurses who care for patients with highly contagious diseases requiring biocontainment.

Abstract Delivering a preterm baby who is admitted to a neonatal intensive care unit can be an enormous hardship for parents and families, and especially for mothers. The consequences of prematurity alter the parental role, affect their confidence in caring for the baby, and subsequently may impact infant outcomes. Adequately educating mothers of premature babies using an evidence-based practice approach may help them gain the confidence and skills needed to care for their infants. The purpose of this project was to create a tool kit to educate mothers of premature babies about the essential components of caring for their babies, establish content validity of the tool kit among clinical experts, and make recommendations about the use of the tool kit in the neonatal intensive care unit. Polit, Beck, and Owen’s framework was used to establish content validity. Neonatal intensive care nurses who were considered “experts” using Benner’s novice-to-expert theory (n = 7 reviewed the tools which were quantitatively computed and yielded an Item Content Validity Index value range of 0.86 to 1.00, and a Scale Content Validity Index of 0.97, reflecting that the content met the objectives of the toolbox. Positive social change can be realized through use of the tool kit in the neonatal intensive care unit to educate mothers in the care of their preterm babies, thus improving both maternal and infant outcomes.

Classification is a data mining problem that arises in many real-world applications. A popular approach to tackle these classification problems is using an ensemble of classifiers that combines the collective knowledge of several classifiers. Most popular methods create a static ensemble, in which a single ensemble is constructed or chosen from a pool of classifiers and used for all new data instances. Two factors that have been frequently used to construct a static ensemble are the accuracy of and diversity among the individual classifiers. There have been many studies investigating how these factors should be combined and how much diversity is required to increase the ensemble's performance. These results have concluded that it is not trivial to build a static ensemble that generalizes well. Recently, a different approach has been undertaken: dynamic ensemble construction. Using a different set of classifiers for each new data instance rather than a single static ensemble of classifiers may increase performance since the dynamic ensemble is not required to generalize across the feature space. Most studies on dynamic ensembles focus on classifiers' competency in the local region in which a new data instance resides or agreement among the classifiers. In this thesis, we propose several other approaches for dynamic class prediction. Existing methods focus on assigned labels or their correctness. We hypothesize that using the class probability estimates returned by the classifiers can enhance our estimate of the competency of classifiers on the prediction. We focus on how to use class prediction probabilities (confidence) along with accuracy and diversity to create dynamic ensembles and analyze the contribution of confidence to the system. Our results show that confidence is a significant factor in the dynamic setting. However, it is still unclear how accurate, diverse, and confident ensemble can best be formed to increase the prediction capability of the system. Second, we propose a system for dynamic ensemble classification based on a new distance measure to evaluate the distance between data instances. We first map data instances into a space defined by the class probability estimates from a pool of two-class classifiers. We dynamically select classifiers (features) and the k-nearest neighbors of a new instance by minimizing the distance between the neighbors and the new instance in a two-step framework. Results of our experiments show that our measure is effective for finding similar instances and our framework helps making more accurate predictions. Classifiers' agreement in the region where a new data instance resides has been considered a major factor in dynamic ensembles. We postulate that the classifiers chosen for a dynamic ensemble should behave similarly in the region in which the new instance resides, but differently outside of this area. In other words, we hypothesize that high local accuracy, combined with high diversity in other regions, is desirable. To verify the validity of this hypothesis we propose two approaches. The first approach focuses on finding the k-nearest data instances to the new instance, which then defines a neighborhood, and maximizes simultaneously local accuracy and distant diversity, based on data instances outside of the neighborhood. The second method considers all data instances to be in the neighborhood, and assigns them weights depending on the distance to the new instance. We demonstrate through several experiments that weighted distant diversity and weighted local accuracy outperform all benchmark methods.

Variable selection procedures for high dimensional data have been proposed and studied by a large amount of literature in the last few years. Most of the previous research focuses on the selection properties as well as the point estimation properties. In this paper, our goal is to construct the confidence intervals for some low-dimensional parameters in the high-dimensional setting. The models we study are the partially penalized linear and accelerated failure time models in the high-dimensional setting. In our model setup, all variables are split into two groups. The first group consists of a relatively small number of variables that are more interesting. The second group consists of a large amount of variables that can be potentially correlated with the response variable. We propose an approach that selects the variables from the second group and produces confidence intervals for the parameters in the first group. We show the sign consistency of the selection procedure and give a bound on the estimation error. Based on this result, we provide the sufficient conditions for the asymptotic normality of the low-dimensional parameters. The high-dimensional selection consistency and the low-dimensional asymptotic normality are developed for both linear and AFT models with high-dimensional data.

The vast and complex wealth of information available to researchers often leads to a systematic review, which involves a detailed and comprehensive plan and search strategy with the goal of identifying, appraising, and synthesizing all relevant studies on a particular topic. A meta–analysis, conducted ideally as part of a comprehensive systematic review, statistically synthesizes evidence from multiple independent studies to produce one overall conclusion. The increasingly widespread use of meta–analysis has led to growing interest in meta–analytic methods for rare events and sparse data. Conventional approaches tend to perform very poorly in such settings. Recent work in this area has provided options for sparse data, but these are still often hampered when heterogeneity across the available studies differs based on treatment group. Heterogeneity arises when participants in a study are more correlated than participants across studies, often stemming from differences in the administration of the treatment, study design, or measurement of the outcome. We propose several new exact methods that accommodate this common contingency, providing more reliable statistical tests when such patterns on heterogeneity are observed. First, we develop a permutation–based approach that can also be used as a basis for computing exact confidence intervals when estimating the effect size. Second, we extend the permutation–based approach to the network meta–analysis setting. Third, we develop a new exact confidence distribution approach for effect size estimation. We show these new methods perform markedly better than traditional methods when events are rare, and heterogeneity is present.

Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART). However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes. Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM- or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships.