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The COVID-19 pandemic has exacerbated unmet mental health needs among adults in the U.S and resulted in significant strains on the U.S. healthcare system. This descriptive, quantitative study aims to investigate reports of unmet mental health needs among adults in the U.S. prior to, and after the onset of the COVID-19 (SARS-CoV-2) pandemic. The purpose of this study is to critically examine state level characteristics and public health response approaches to better understand the contributing factors to mental illness and unmet mental health needs in the U.S. The specific objectives of this study include 1) To create a comprehensive national, longitudinal dataset; 2) To investigate state level variability in regards to mental health outcomes, contrasting states with better and worse mental health indicators; 3) To examine COVID-19 response legislation on mental illness (depression), contrasting states with more restrictive and less restrictive COVID-19 response measures, and 4) To provide an in-depth comparison of the best and worst ranked states.
A major component of this dissertation is the development of a comprehensive state-level dataset that links key state characteristics related to mental illness and COVID-19 response measures to aggregate individual self-report mental health data. The dataset (n=50) consists of 206 total variables sourced from 8 data sources. Descriptive statistics, frequencies, and bivariate analyses were run in SPSS Statistics 28 to determine if there were any correlations among state level characteristics, COVID-19 response measures, and unmet mental health needs. Findings suggest slight correlations among meso- and macrosystem level variables which could be indicative of the impacts of the COVID-19 pandemic on economic and mental health outcomes. Economic characteristics at the macro-system level, such as household income and healthcare spending, look to be associated with better mental health rankings.
This dissertation research provides an original contribution to the field of public health as there is minimal existing literature pertaining to the influence of state level variability on mental illness and unmet mental health needs. This research also provides the groundwork for future studies to build upon the data collected on state level factors which influence mental health outcomes, and to explore the inter-relationships between the U.S. healthcare and economic systems. In terms of health policy, this data and subsequent research will provide guidance for improvements regarding mental health advocacy and reform efforts.
AMY BIANG. Examining The Lived Experiences Of Counselors Of Color In The Eating Disorder Field: A Post-Intentional Phenomenological Study. (Under the direction of DR. CLARE MERLIN-KNOBLICH)
Though eating disorders (EDs) affect a diverse population, among professionals who treat EDs, Counselors of Color (COC) are under-represented (Jennings- Mathis et al., 2020). Because the ED field is predominately comprised of White professionals (AED, 2022; Jennings-Mathis et al., 2020), a danger exists that White invisibility hinders counselors and researchers from recognizing oppression and injustices that occur in the ED field. The purpose of this Post-Intentional Phenomenological study was to bring awareness of the experiences of COC in the ED field and create a dialogue for systemic and social change related to their experiences. Eleven participants were interviewed, and the interview material was analyzed using a post-intentional phenomenological design. Tentative manifestations, provocations, and productions emerged through a whole-part-whole analysis. Five tentative manifestations; unprepared, belonging, unspoken knowing, exhaustion, and microaggressions; seven provocations; vulnerability, race as an asset, complexity, sense of duty, nonmaleficence, double bind, and credibility, and two productions; cultural inclusion and fulfillment, offer valuable knowledge about the experiences of COCs in the eating disorder field. Implications for counselor education and the ED profession are discussed, along with limitations and future research considerations.
This research uses PPP loan data from the Small Business Administration (SBA) to investigate whether information frictions contributed to disproportionate PPP loan disbursements to certain racial and socioeconomic groups. This analysis makes several contributions. First, it adds to the body of literature on the PPP program, the impact of COVID-19 on small businesses, and government subsidy programs designed to mitigate economic crises. Prior research on the PPP program examined whether loans were allocated to business owners based on socio-demographic factors. Atkins et al. (2022) find a negative relationship between a community’s minority share of business owners and disbursed PPP loan amounts. Likewise, Howell et al. (2021) report that minority business owners were less likely to obtain PPP loans. We build on these existing studies by conceptualizing information frictions. To our knowledge, this is the first study to conceptualize information frictions into three main drivers, socio-demographic bias, financial institution access, and digital literacy, and to explain the relationship between information frictions and the efficacy of the PPP program.
How statistics are wielded and presented in the real world cannot be separated from the fact that social issues operate within systems of marginalization, privilege, and power. Thus, statistical literacy necessitates the application of a true critical lens. Continued calls for critical statistical literacy from a consumer orientation within K-16 education, points to the need for research on how critical statistical literacy is enacted, particularly among the population of preservice mathematics teachers responsible for answering such calls. This study employed case study methodologies to gain deeper insight into how secondary preservice mathematics teachers enact Critical Statistical Literacy Habits of Mind (CSLHM) when making sense of data representations from the media. Critical Statistical Literacy Habits of Mind (CSLHM) are the thinking behaviors called upon to make sense of statistical messages with a specific focus on how the statistics and/or statistical message are used to uphold or dismantle structures of inequity. Findings reveal that preservice teachers emergently enact CSLHM. Some preservice teachers enact particular CSLHM robustly, although not habitually. Broader implications include the need to support preservice teachers’ development of CSLHM so that they can support their students to do the same.
In this dissertation, we propose a broad class of so-called Cox-Aalen transformation models that incorporate both multiplicative and additive covariate effects on the baseline hazard function through a transformation framework. The proposed model offers a high degree of flexibility and versatility, encompassing the Cox-Aalen model and transformation models as special cases. For right-censored data, we propose an estimating equation approach and devise an Expectation-Solving (ES) algorithm that involves fast and robust calculations. The resulting estimator is shown to be consistent and asymptotically normal via empirical process techniques. Finally, we assess the performance of the proposed procedures by conducting simulation studies and applying them in two randomized, placebo-controlled HIV prevention efficacy trials.
We also consider the regression analysis of the Cox-Aalen transformation models with partly interval-censored data, which comprise exact and interval-censored observations. We construct a set of estimating equations and implement an ES algorithm that ensures stability and fast convergence. Under regularity assumptions, we demonstrate that the estimators obtained are consistent and asymptotically normal, and we propose using weighted bootstrapping techniques to estimate their variance consistently. To evaluate the proposed methods, we perform thorough simulation experiments and apply them to analyze data from a randomized HIV/AIDS trial.
Human trafficking is an emergent public health concern that, as noted by the National Institute of Justice (2021), receives attention and support from human rights advocates and law enforcement agencies. The trafficking of women in the sex industry is a growing health concern, as most victims are often unrecognized when seeking healthcare services. Sex-trafficked women suffer adverse health effects and often present to healthcare facilities while still under the control of their traffickers (Rapoza, 2022). A review of the literature revealed a deficit in clinicians' abilities to recognize this vulnerable population. This scholarly project aimed to determine how participation in an educational intervention affects providers’ and clinicians’ knowledge of the facilitators and barriers to identifying and intervening with pregnant sex-trafficking victims. The intervention included the implementation of an educational intervention to enhance knowledge. A pre and posttest design was used to measure a change in confidence, knowledge, and skills. A Likert survey to assess confidence and knowledge of sex trafficking was administered before and 30 days after the educational intervention. This project aimed to demonstrate that education increased confidence, knowledge, and skills among obstetric public health providers and clinicians regarding the identification of sex-trafficked victims.
The next pandemic is already underway in the proliferation of antimicrobial resistance (AMR) genes. Evolutionary principles guide this ``silent pandemic'', resulting in multidrug resistant (MDR) bacteria that resist three or more classes of antimicrobial compounds. One hypothesis for the development of MDR Escherichia coli (E. coli) theorizes that resistance results from increased mutations attributed to bacteria with a deficient Mutator S gene.
First, I used phylogenetic comparative analyses on the mutS genes from 817 high-quality E. coli isolates. Although I observed 271 MDR isolates in this data set, I found no evidence for a deficient mutS gene. Additionally, when modeling the coevolution of MDR and variant residues in the MutS protein, the evidence supported independent evolution between the traits.
To understand this confounding result, I trained five random forest estimators to predict AMR, achieving a mean ROC AUC of 0.87 +/- 0.04 on 66 features engineered from 5511 annotated genes in the pangenome. The top performing predictors did not include mutS, but instead genes associated with horizontal gene transfer. This result supports the role of accessory genes in spreading MDR. My work demonstrates the combined usefulness of phylogenetic methods and machine learning to arrive at hypotheses for polygenic traits.
The increasing prevalence of roads and vehicle traffic, most particularly in urban areas, has a corresponding impact on road mortality, especially for avian species that make use of foraging opportunities along roadside verges. In many cases, raptors, or birds of prey, are vulnerable to vehicle collisions because they forage along roads. The purpose of my research was to conducted a comprehensive investigation into the traffic, habitat and road verge factors that influence collision risk for both nocturnal and diurnal raptors. In addition, I examined the impact that species and individual traits have on the location of vehicle collisions involving birds of prey. I expected to find a notable difference in collision vulnerability between nocturnal and diurnal species. I also expected that road verge vegetation would play a significant role in vehicle collision risk for birds of prey.
Although I did not observe a significant difference in collision risk for raptors based on time of activity, I did find that prey cover in the form of complex vegetation along road verges was an important predictor of collision risk. Dense brush, shrubs or tall grass provide habitat for prey items such as small birds and mammals, which in turn attracts foraging raptors to roadsides, thus increasing the risk of being struck by a passing vehicle.
My analysis of species and individual traits showed that body size and reproductive output were the most important predictors of collision risk. Larger species and those with smaller clutch sizes were most likely to be hit by cars, regardless of road and road verge conditions or habitat characteristics.
Studying urban dynamics is essential given the ever-increasing changes in urban areas with all its ensuing consequences, whether negative or positive. It is of paramount importance to take into account the temporal dimension of urban dynamics when studying its patterns and processes. Nevertheless, the majority of studies overlook this consideration and take cross-sectional research approaches. Moreover, a large body of literature in urban dynamics is dedicated to the explanatory analysis and causal inference only, neglecting the importance of predictive analysis. Addressing these two main gaps, this research explores urban dynamics through both causal inference and predictive modeling using longitudinal research designs. Urban dynamics are studied from two aspects in this work; transportation/land-use interactions, and economic growth. In the first article, the impact of built environment on commuting duration is assessed in 2000 and 2015 in Mecklenburg County, NC using spatial panel data models. Results show that the built environment has a statistically significant impact on commuting duration. However, it is important to note that the practical magnitude of the impact is small. In the second and third articles, the business performance of businesses are forecasted for non-business services and business services respectively in Mecklenburg County, NC, using recurrent neural networks long short-term memory deep learning method. After building and training the sequential model, its predictive performance is assessed using out-of-sample evaluation.
With the rapid increase in connected devices and SoC design architecture being used in diverse platforms, they become potential targets to gain unauthorized access for data and privacy invasion. Therefore, heterogeneous SoC architecture raises security concerns in addition to the benefits they offer with improved throughput. They are susceptible to side-channel attacks where secure information is extracted through communication channels. Crypto algorithms implemented for secure authentication tend to leak sensitive information jeopardizing system security. Memory corruption vulnerabilities, code injection, buffer overflow attacks and other software-based attacks through untrusted channels tend to control the flow of the application with malicious data. Though traditional defense mechanisms have been implemented, they are still vulnerable to side-channel attacks. Secure measures to protect the interfaces and data propagation through different channels are critical and building a resilient model consists of the on-chip security factors. In this work, a platform-based SoC model is implemented to meet the security objectives using the RISC-V architecture. An information flow tracking module tracks the flow of data for the system’s integrity along with crypto engines and a secure boot mechanism for secure device authentication providing encrypted data transfers. For bitstream resilient SoC models the work extends a logic obfuscation module with runtime security leading to a secure assessment framework. This work explores the microarchitectural side-channel attacks with machine learning models.
