Matching Items (81)
Description
A void exists in public administration, criminology, and criminal justice research as it relates to the study of power in American policing agencies. This has significant ramifications for academia and practitioners in terms of how they view, address, study, and interpret behaviors/actions in American policing agencies and organizations in general. In brief, mainstream research on power in organizations does not take into account relationships of power that do not act directly, and immediately, on others. By placing its emphasis on an agency centric perspective of power, the mainstream approach to the study of power fails to recognize indirect power relationships that influence discourse, pedagogy, mechanisms of communication, knowledge, and individual behavior/actions. In support of a more holistic inquiry, this study incorporates a Foucauldian perspective of power along with an ethnographical methodology and methods to build a greater understanding of power in policing organizations. This ethnography of an American policing organization illuminates the relationship between the exercise of power and the objectification of the subject through the interplay of relationships of communication, goal oriented activities, and relationships of power. Specifically, the findings demonstrate that sworn officers and civilian employees are objectified distinctly and dissimilarly. In summary, this study argues that the exercise of power in this American policing organization objectifies the civilian employee as a second class citizen.
ContributorsBentley, Paul C (Author) / Catlaw, Thomas (Thesis advisor) / Musheno, Michael (Committee member) / Lucio, Joanna (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Mixed-income housing policy has been an approach to address the problem of concentrated poverty since the 1990s. The idea of income mix in housing is founded on the proposition that economic opportunities of the poor can be expanded through the increasing of their social capital. The current in-depth case study of Vineyard Estates, a mixed-income housing development in Phoenix, AZ tests a hypothesis that low-income people improve their chances of upward social mobility by building ties with more affluent residents within the development. This study combines qualitative and quantitative methods to collect and analyze information including analysis of demographic data, resident survey and in-depth semi-structured interviews with residents, as well as direct observations. It focuses on examining the role of social networks established within the housing development in generating positive economic outcomes of the poor. It also analyzes the role of factors influencing interactions across income groups and barriers to upward social mobility. Study findings do not support that living in mixed-income housing facilitates residents' upward social mobility. The study concludes that chances of upward social mobility are restrained by structural factors and indicates a need to rethink the effectiveness of mixed-income housing as an approach for alleviating poverty.
ContributorsDurova, Aleksandra (Author) / Kamel, Nabil (Committee member) / Pfeiffer, Deirdre (Committee member) / Lucio, Joanna (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012
Description
Government performance and accountability have grown to be predominant areas within public administration literature over the last forty years. The research presented in this dissertation examines the relationship between citizen satisfaction and local government performance. Citizen review of service delivery provides vital feedback that facilitates better resource management within local government. Using data from a single jurisdiction, two aspects of citizen satisfaction are reviewed. This includes citizen review of overall city performance, and citizen satisfaction with individual service delivery. Logit regression analysis is used to test several factors that affect citizen evaluation of service delivery in local government, while ordinary least squares regression is used to test the relationship between personal factors and citizen evaluation of specific local services. The results generated four major findings that contribute to the scholarly body of knowledge and local government knowledge application. First, citizens who are predisposed to supporting the local jurisdiction are more likely to rate service delivery high. Second, customer service is important. Third, those who experience government services similarly will collectively react similarly to the service experience. Finally, the length of residency has an impact on satisfaction levels with specific services. Implications for the literature as well as for practice are discussed.
ContributorsMcNamara, Catherine (Author) / Alozie, Nicholas O (Thesis advisor) / Cayer, Joseph (Thesis advisor) / Lucio, Joanna (Committee member) / Arizona State University (Publisher)
Created2012
Description
Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well.
ContributorsVankipuram, Mithra (Author) / Greenes, Robert A (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Petitti, Diana B. (Committee member) / Dinu, Valentin (Committee member) / Smith, Marshall L. (Committee member) / Arizona State University (Publisher)
Created2012
Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
Description
With more than one third of Americans considered to be obese, obesity is a public health issue in the United States. While obesity is linked to and caused by a number of factors, sugar sweetened beverage (SSB) consumption is a major contributor to increased obesity rates. For the purposes of this paper, SSBs will include any beverage in which sugar is added. This includes juices that are not 100% fruit juice, coffee or tea drinks that are sugar sweetened, energy or sport drinks, and most commonly, soda. Excess sugar in the diet is substantially linked to obesity and negative health effects. SSBs represent the primary source of added sugar in the average American diet. Part I of this paper will discuss obesity as a public health problem and establish the link between consumption of SSBs and poor health effects. Part II will discuss the public policy instrument families and the strengths and weaknesses of each policy approach. Part III will identify current policies specifically focused on curbing SSB consumption. Each policy will be analyzed for efficacy based on available scientific research. Lastly, Part IV will propose new policy alternatives and ways to improve current policies. A final policy recommendation will be presented as an ideal roadmap for policy makers looking to address the link between SSB consumption and obesity.
ContributorsSaria, Matthew Ricardo (Author) / Lucio, Joanna (Thesis director) / Holland, Thomas (Committee member) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05