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Evaluating a Mental Health Crisis Intervention Program in a Rural Community Health Center
No full textMental illness is one of the leading disease burdens in the United States. In southeast Kansas, there are high rates of mental illness, substance abuse, and poverty, coupled with a health care provider shortage areas. The Community Health Center of Southeast Kansas (CHCSEK) in Pittsburg, Kansas, is a Federally Qualified Health Center that provides integrated healthcare in an underserved rural population area. The clinic developed the Management of Behavioral Health Emergencies procedure to help clinic staff respond and coordinate care for patients in a mental health crisis. The project objective was to assess the effectiveness, efficiency, and feasibility of a mental health crisis intervention in a rural mental health clinic. The study objectives include: a) reviewing the timeframe of a patient encounter and the timeframe for accessing emergency services and or a consultation; b) reviewing how both patient and staff safety were assured; c) evaluating if emergency services were obtained during the mental health crisis patient; and d) if CHCSEK staff collaborated with any outside emergency services and hospitals. A descriptive case study research design was used for this project. Data collection included information obtained from patient charts, incident reports, staff observations, and an anonymous online staff survey. The cases were chosen by the Behavioral Health and Addiction Services Clinic Director. Both exemplar and outlier cases of patients that presented to the Pittsburg clinic in mental health crisis occurring from November 2018 through August 2020 were chosen for this study. Data analysis followed theoretical propositions to answer the project objective. The concepts of time, safety and efficiency were analyzed. The comprehensive evaluation provided feedback and recommendations to clinic administration, behavioral health providers, and other stakeholders involved in managing patients in a mental health crisis at CHCSEK. The recommendations emphasized promoting high quality behavioral health care, patient and staff safety, and improved patient healthcare outcomes
The Power and Politics of Cybersecurity: A Quantitative Study of Federal Cash Windfall Allocation as a Measure of Impact on Comprehensive Cybersecurity Posture
No full textIn their attempts to create a comprehensive cybersecurity posture, chief information security officers (CISOs) can only be as effective as the resources they garner. In the federal context, budgets and spends are ultimately under the auspices of the agency heads who set priorities and direction. This study sought to gain insight on the impact of organizational power and politics in the cybersecurity post-budgetary process within U.S. federal government agencies through a comparative examination of budgeted versus actual spending. It addressed one research question: To what extent do power and politics impact the federal cybersecurity budgetary cash windfall allocation and the resultant organizational cybersecurity posture? The literature of organizational power and politics establishes means to measure the impact of individual and group power on budgets, funding, allocations, expenditures, and gamesmanship. Applied in the federal cybersecurity arena, the impact of power and politics on budgets and spend can be measured to better understand and mitigate risk factors in cybersecurity posture. A quantitative cross-sectional causal-comparative approach with a CISO survey was leveraged to study the topic ex post facto. The study utilized three phases of data collection from publicly available sources and primary data collection, as well as five phases of data analysis covering 2009 to 2016, to examine civilian cabinet-level agencies across the executive branch of the federal government. Findings showed that most agencies were budgeting cybersecurity in a comprehensive fashion. However, actual expenditures were significantly reduced from budgetary allocations and remained focused on the area of technology, leaving the people, process, and policy aspects of cybersecurity posture at times unfunded. Further, the results showed that the agency head and CISO had little to no power or political connectedness and there were intractable barriers against improving their dyadic relationship. The CISO’s career at the agency and political awareness, among other factors, were statistically significant in predicting the differences of cybersecurity technology budgets and spends, but the greatest effect was seen in agency head connectedness and political connectedness. Considering the vital importance of the CISO in the federal sphere, these findings point to issues that need to be further studied and addressed to effectuate a comprehensive cybersecurity posture
Empirical Analysis of Incentives and Selection in Health Care Markets
No full textThe common theme throughout these collection of essays is the analysis of incentives and selection effects in health care markets. The first chapter investigates the effect that a behavioral nudge when choosing health care plans can have on risk selection into High-Deductible Health Plans (HDHPs). I use novel administrative data on enrollee plan choices and claims from a large employer that implemented an automatic enrollment policy, changing the default option to the company’s HDHP. My findings show that the automatic enrollment policy more than doubled the fraction of HDHP enrollees but did not mitigate the risk selection of low-cost individuals. I then formulate a machine learning framework to test for the presence of private information, where the results confirm a significant degree of private information leading to the overprediction of costs for HDHP switchers. The second chapter studies how changing the cost-sharing differences between in-network and out-of-network care under a health insurance plan affects how spending and utilization is allocated across in versus out-of-network providers. I exploit a quasi-experimental design based on a large employer that merged employees on the PPO and POS health plans together, holding fixed the network design while changing the relative cost-sharing generosities between in and out-of-network care. The results indicate that changing the cost-sharing attributes had an insignificant effect on in-network and out-of-network spending. The third chapter investigates how network breadth is affected by the number of insurance options in California’s Medicaid Managed Care system. Using within county variation in the number of insurers over the years 2002-2011, I estimate a 0.515 percentage point decrease in the probability that a hospital is in a plan’s network for every insurer a county adds. The results suggest that insurers are disincentivized from offering a broad network as they face more competitors
Machine Learning-Based Cost Predictive Model for Better Operating Expenditure Estimations of U.S. Light Rail Transit Projects
No full textInaccurate forecasts of operating expenditures during the planning phase for new Light Rail Transit (LRT) projects in the United States underestimated future costs by up to 45% (Pickrell, 1989). When operating expenditures exceeded projected levels, local transit agencies often reduced public transit services to operate within their respective annual budgets. Therefore, it is imperative for transit agencies to produce reasonably accurate planning estimates to secure sufficient funding to support future operations, maintenance, and service delivery associated with LRT systems. The research aimed to develop a more accurate LRT operating expenditure predictive model to be used during the planning stage. Traditional statistical analysis and various machine learning-based algorithms were utilized with input from 22 LRT systems in the United States spanning between 2008 to 2018 from various U.S. governmental public databases. This praxis extended the current state of practice that relied primarily on sum of unit-cost estimates (also known as the unit-cost method) which generally failed to produce accurate forecasts due to lack of engineering details at the planning stage. Existing research attempted to develop regression-based methodologies using system-based attributes but did not substantially increase prediction accuracy from using the unit-cost method. The research improved current practices and research by having developed a more accurate and replicable machine learning-based predictive model using available geographic, socio-economic and LRT system-related variables
Greater than Half: A Psychoanalytical Approach to Multiracial Characters in Asian American Literature
No full textAlthough Asian American literature frequently features multiracial characters, these narratives rarely represent the experiences of multiracial people. Because the canon of Asian American literature thus far largely focuses on the subjectivities of monoracial Asian American experiences, portrayals of mixed-race Asian Americans largely focus on the perception of mixed-race bodies rather than the intimate nuances of being mixed-race. In depicting mixed-race Asian American bodies as culturally unintelligible, these stories deploy those characters as metaphors for broader multicultural experiences, but fail to explore the lived experiences of mixed-race identity. This project will explore the ways in which multiracial Asian American characters are used to portray projected fears and how, in truth, multiracial experiences are not reducible to the parts of their whole racial identity. This project traces the psychoanalytical origins of the most extreme responses to mixed-race bodies: abjection and fetishization. Key words: miscegenation, mixed-race, multiracial, multicultural, Eurasian, abjection, fetishizatio
Degrees of Relevance: A Basic Qualitative Study of How MBA Students Make Their Education Relevant as They Cross Boundaries Between School and Work
No full textThe purpose of the study was to explore how working professionals enrolled in MBA programs make their education relevant. This inquiry was guided by the following central research question: How do students enrolled in MBA programs make their education relevant as they cross boundaries between school and work? The central research question was supported by three subquestions: What are the objects crossing the boundary between the MBA program and students’ workplaces? How do MBA students broker learning at the boundary? At what level are interactions occurring and to what end? Grounded in social constructivist epistemology, a basic qualitative method was chosen for this study. Data were collected through 18 semi-structured interviews with 10 students and eight learning partners, 18 field notes that described the context of the interview and early insights from the data collected, and 28 documents such as course descriptions and work presentations. Study participants shared a total of 39 critical incidents of learning that crossed the boundary between school and work. Data were analyzed and synthesized to produce three overall themes, which were translated into a typology of four relevance-making types, which provided the basis for 10 participant profiles. Then, patterns of content, process, and outcomes for each type of relevance-making were analyzed and synthesized. This study found that relevance-making differed by type and depended upon students’ intentions for Innovative Climbing, Identity Switching, Introspective Exploring, or Fast Founding. In Innovative Climbing, students integrated new business concepts from school at work in order to earn promotions. In Identity Switching, students changed how they saw themselves and how others saw them in order to change where they work. In Introspective Exploring, students reflected upon their experiences in ways that informed their career goals. In Fast Founding, a student rapidly introduced business concepts from school into the workplace to sustain his business venture. It was through discovering or realizing these intentions that relevance was made
Performance of the New Visual-Spatial Index of the Wechsler Intelligence Scale for Children, Fifth Edition in Children with Williams Syndrome
No full textWilliam Syndrome (WS) is a neurodevelopmental disorder (NDD) known for causing impairment in visual-spatial processing. One of the most widely used pediatric intelligence tests, the Wechsler Intelligence Scale of Children, 5th Edition (WISC-V), added the Visual-Spatial Index (VSI), which measures visual-spatial processing (Wechsler, 2014). The VSI has yet to be studied within the WS population. The study utilized a three-group design, including WS, a clinical comparison group of Autism Spectrum Disorders (ASD), and a typically developing (TD) comparative control. The study explored differences in group performance on the VSI. The study also compared group performance on the VSI to a well-established measure of visual-spatial processing, the Beery-Buktenica Developmental Test of Visual-Motor Integration, 6th Edition (Beery-VMI, Beery et al., 2010). Results showed a significant difference (p = .006) in WS group performance on the two tasks (Block Design and Visual Puzzles) which comprise the VSI. Also, the VSI performance in WS was significantly different (p < .0001) than the ASD and TD groups. Finally, group performance on the VSI and Beery-VMI significantly correlated (r = .783). Overall, the VSI and Beery-VMI measured variations in visual-spatial abilities, with group performance patterns yielding similar output on both. Implications of the classification and clinical performance of the VSI and Beery-VMI are discussed
The Roles of Plasmepsins IX and X in Malaria Parasite Biology
No full textProteases of the malaria parasite Plasmodium falciparum have been targeted for drug discovery for decades. The P. falciparum genome encodes ten aspartic proteases called plasmepsins, which are involved in diverse cellular processes. In this work we address the roles of two of these plasmepsins, plasmepsins IX and X (PM IX and X), the two least studied aspartic proteases in blood stage malaria parasites till date. We explore the essentiality of these proteases in parasite development, attempt to identify their substrates and the ability to drug them. We show that PM IX is essential for erythrocyte invasion, acting on rhoptry secretory organelle biogenesis. When PM IX is knocked down, rhoptry formation is impaired but the substrate(s) of PM IX responsible for this phenotype have not yet been discovered. We have suspected targets and the progress made on implicating these important substrates will be reported in this work. In contrast, PM X is essential for both egress and invasion, controlling maturation of the subtilisin-like serine protease SUB1 in exoneme secretory vesicles. SUB1, synthesized as a zymogen needs to be converted to its active form after an initial self-cleavage. We explore the mechanism by which PM X converts the intermediate SUB1 species to its active form. Interestingly, we discover that PM X cleaves the intramolecular self-inhibiting prodomain part of the protein probably allowing a second activation step. We proceed to determine the cleavage sites that could prevent the inhibitory function of the prodomain. We have identified compounds with potent antimalarial activity targeting PM X, including a compound known to have oral efficacy in a mouse malaria model
Leadership, Quality Improvement, Team Functionality, and HIV Viral Load Suppression in Uganda
No full textLow viral load suppression rate is a significant public health issue in Uganda and similar countries. A nationwide quality improvement (QI) initiative was implemented from January 2019 to improve viral load suppression. Although QI team characteristics have been shown to influence the success of such QI initiatives, no studies have been found to understand how they influence the success of QI efforts to improve HIV viral load suppression in Uganda. The purpose of this cross-sectional, quantitative study was to determine whether there is a significant association between HIV clinic leader involvement in QI teams, QI team functionality, QI team diversity, QI team skill, and HIV viral load suppression rates in Uganda, controlling for age, sex, and health facility type. The study was grounded in the model for success in quality improvement (MUSIQ) and the chronic care model (CCM). Secondary data for 2,758 patients attending 18 HIV clinics across three regions in Uganda were abstracted from the health management information system. Sampling was at the health facility level so that all patients in each sampled clinics were included. Data were analyzed using logistic regression, with one binary dependent variable of viral load suppression recorded as suppressed or unsuppressed. Leadership involvement, team functionality, patient age, patient sex, and health facility type were significantly associated with viral load suppression (p < 0.05 for each). QI initiatives should invest in QI team characteristics because they affect patient outcomes. This study could potentially impact social change in low-income settings by improving service delivery for people with HIV in Uganda so that they achieve viral load suppression
Accelerating the Understanding and Design of Intracellular Biosensors by Massively Multiplexed Experimentation and Machine Learning
No full textRecent progress in large-scale DNA synthesis and next-generation DNA sequencing technology have enabled studies of biological processes at a massive scale. These studies can be further coupled to advanced computational methods using machine learning to explore and reveal essential elements of biological function. Two separate factors controlling gene expression are studied here using such a paradigm: the lac repressor protein which can regulate transcription of DNA, and a riboregulatory toehold switch that can control translation. The function of the lac repressor is interesting since it intrinsically couples the binding of a small molecule to the binding of DNA and has emerged as a useful tool in synthetic biology as an intracellular biosensor. A deep neural network was developed to predict transcriptional repression mediated by the lac repressor, using 43,669 experimental measurements of variant function. When validated across ten separate training and testing splits of single mutations in the lac repressor, our best performing model achieved a median Pearson correlation of 0.79, exceeding any previous model. Deep representation learning approaches, first trained in an unsupervised manner across millions of diverse proteins, can be fine-tuned in a supervised fashion using lac repressor experimental datasets to more effectively predict a variant effect on repression. Separately, engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. To facilitate understanding and design of one such RNA element, the toehold switch, we synthesized and characterized in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. Deep neural networks trained on nucleotide sequences outperform (R2=0.43-0.70) previous state-of-the-art thermodynamic and kinetic models (R2=0.04-0.15) and allow for human-understandable attention-visualizations to identify success and failure modes. Thus, two factors controlling gene expression are studied here in the context of large-scale mutational scanning and machine learning in order to understand and design such factors toward effective intracellular biosensing