The illness experiences of African Americans residing in an inner city community, Houston, Texas

This dissertation documents health and illness in the context of daily life circumstances and structural conditions faced by African American families living in Clover Heights (pseudonym), an inner city public housing project in the Third Ward, Houston, Texas. Drawing from Kleinman's (1980) model of culturally defined health care systems and using the holistic-content approach to narrative analysis (Lieblich, Tuval- Mashiach, & Zilber, 1998) the purpose of this research was to explore the ways in which social and health policy, economic mobility, the inner city environment, and cultural beliefs intertwined with African American families' health related ideas, behaviors, and practices. I recruited six families using a convenience sampling method (Schensul, Schensul, & LeCompte, 1999) and followed them for fourteen months (2010–2011). Family was defined as a household unit, or those living in the same residence, short or long-term. Single, African American women ranging in age from 29–80 years headed all families. All but one family included children or grandchildren 18 years of age and younger, or children or other relative 18 years of age and older. I also recruited six residents with who I became acquainted over the course of the project. I collected data using traditional ethnographic methods including participant-observation, archive review, field notes, mapping, free-listing, in-depth interviews, and life history interviews. ^ Doing ethnography afforded the families who participated in this project the freedom to construct their own experiences of health and illness. My role centered on listening to, learning from, and interpreting participants' narratives, exploring similarities and differences within and across families' experiences. As the research progressed, a pattern concerning diagnosis and pharmacotherapy for children's behavioral and emotional problems, particularly attention-deficit hyperactivity disorder (ADHD) and pediatric bipolar disorder (PBD), emerged from my formal interactions with participants and my informal interactions with residents. The findings presented in this dissertation document this pattern, focusing on how mothers and families interpreted, organized, and ascribed meaning to their experiences of ADHD and PBD. ^ In the first manuscript presented here, I documented three mothers' narrative constructions of a child's diagnosis with and pharmacotherapy for ADHD or PBD. Using Gergen's (1997) relational perspective I argued that mothers' knowledge and experiences of ADHD and PBD were not individually constructed, but were linguistically and discursively constituted through various social interactions and relationships, including family, spirituality and faith, community norms, and expert systems of knowledge. Mothers' narratives revealed the complexity of children's behavioral and emotional problems, the daily trials of living through these problems, how they coped with adversity and developed survival strategies, and how they interacted with various institutional authorities involved in evaluating, diagnosing, and encouraging pharmaceutical intervention for children's behavior. The findings highlight the ways in which mothers' social interactions and relationships introduced a scientific language and discourse for explaining children's behavior as mental illness, the discordances between expert systems of knowledge and mothers' understandings, and how discordances reflected mothers' ‘microsources of power’ for producing their own stories and experiences. ^ In the second manuscript presented here, I documented the ways in which structural factors, including gender, race/ethnicity, and socioeconomic status, coupled with a unique cultural and social standpoint (Collins, 1990/2009) influenced the strategies this group of African American mothers employed to understand and respond to ADHD or PBD. The most salient themes related to mother-child relationships coalesced around mothers' beliefs about the etiology of ADHD and PBD, ‘conceptualizing responsibility,’ and ‘protection-survival.’ The findings suggest that even though mothers' strategies varied, they were in pursuit of a common goal. Mothers' challenged the status quo, addressing children's behavioral and emotional problems in the ways that made the most sense to them, specifically protecting their children from further marginalization in society more so than believing these were the best options for their children.^

Choosing and Maintaining Programs for Sex Education in Schools: The CHAMPSS Model

Background: Despite effective solutions to reduce teen birth rates, Texas teen birth rates are among the highest in the nation. School districts can impact youth sexual behavior through implementation of evidence-based programs (EBPs); however, teen pregnancy prevention is a complex and controversial issue for school districts. Subsequently, very few districts in Texas implement EBPs for pregnancy prevention. Additionally, school districts receive little guidance on the process for finding, adopting, and implementing EBPs. Purpose: The purpose of this report is to present the CHoosing And Maintaining Programs for Sex education in Schools (CHAMPSS) Model, a practical and realistic framework to help districts find, adopt, and implement EBPs. Methods: Model development occurred in four phases using the core processes of Intervention Mapping: 1) knowledge acquisition, 2) knowledge engineering, 3) model representation, and 4) knowledge development. Results: The CHAMPSS Model provides seven steps, tailored for school-based settings, which encompass phases of assessment, preparation, implementation, and maintenance: Prioritize, Asses, Select, Approve, Prepare, Implement, and Maintain. Advocacy and eliciting support for adolescent sexual health are also core elements of the model. Conclusion: This systematic framework may help schools increase adoption, implementation, and maintenance for EBPs.

Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^