Dissertation Defense Announcements

Candidate Name: Tristin L. White
Title: Counselor Posttraumatic Growth Linked to Harmful Clinical Supervision
 October 31, 2022  10:00 AM
Location: Virtual (email tlorrain@uncc.edu for Zoom link)
Abstract:

Clinical supervision is the primary method to educate and train professional counselors (Baltrinic & Wachter Morris, 2020). While clinical supervision tends to be positive and constructive, harmful clinical supervision occurs. As defined by Ellis et al. (2014a), harmful clinical supervision includes any inappropriate action or inaction by the supervisor that causes psychological, emotional, or physical harm or trauma to the supervisee. Research on harmful clinical supervision is growing (Cook & Ellis, 2021; Ellis et al., 2014a, 2015), but the focus remains on how counselors are traumatized by these experiences (Ellis et al., 2017; McNamara et al., 2017). This qualitative study takes a novel approach using the lens of Tedeschi and Calhoun’s (1996) theory of posttraumatic growth to explore the positive effects of harmful clinical supervision. A sample of 12 licensed counselors completed semi-structured interviews to share their experiences. Five main themes emerged through data analysis: Confusion, Support and Encouragement, Safety and Protection, Financial Security, and Professional Duty. These findings align with the five growth categories described by Tedeschi and Calhoun, but an additional category, Professional Duty, was also identified. This study answers the research questions by providing insight into the context and process of counselor posttraumatic growth. Implications for the profession, study limitations, and suggestions for future research are discussed.



Candidate Name: Aaron Yerke
Title: ON COMPOSITIONALLY AWARE AND NAÏVE APPROACHES TO NORMALIZATION OF 16S MICROBIOME DATA
 October 26, 2022  9:30 AM
Location: https://charlotte-edu.zoom.us/j/99052291735?pwd=RllLMDVtM0xpUVJyamJqNU92bW4wUT09
Abstract:

Compositional data refers to any data that represents parts of a whole, and DNA sequencing data is compositional in nature. This is due to the constraint on our current sequencing technologies that allow us to record a sample of the sequences rather than recording all the sequences. This means that sequencing data breaks the assumption of independence (Gloor et al., 2017). It has been long known that analysis of compositional data is challenging and can lead to spurious correlations. However, DNA sequencing data is inherently noisy due to both limitations of sequencing technology and its biological nature. Read depth, the number of sequencing reads from each sample, is known to be a confounding factor in many studies also plays a role in creating artifacts in this type of data. In this work, we demonstrate that read depth drives variance in four different datasets and propose a method for quantifying artifacts generated by read depth. We use this new method to compare untransformed data, several compositionally aware transformations, and a transformation which we call “lognorm” that normalizes samples by read depth in log space. Ultimately, we find that lognorm consistently had less read depth artifacts than the other transformations.
One way to determine the value of a data transformation is to show that it improves the performance of a machine learning classifier. We compared several common transformations to see if they improve the accuracy of a random forest and found that lognorm consistently significantly improves the accuracy of random forest. We believe that lognorm improves accuracy by reducing read depth artifacts and allows the MLA to learn from smaller signals within the data.



Candidate Name: Cornelia Okraski
Title: The association between first language status and second language teacher edTPA performance, perceptions, and preparation
 November 07, 2022  1:30 PM
Location: Zoom
Abstract:

Due to the shortage of World Language and English as a Second Language teachers, recruiting, retaining, and supporting aspiring second language teachers in the completion of their teacher licensure program is crucial. One barrier to the profession for these teachers is edTPA. Research has suggested that non-native English speakers (NNES) who populate second language teacher preparation programs may struggle to complete this assessment more so than their native-English speaking (NES) peers. To shed light on this topic, the researcher used a mixed methodology to examine the performance, perceptions, and preparation of NNES and NES teacher candidates on the World Language and English as an Additional Language edTPA. Data sources included edTPA scores, survey responses and faculty interviews.

The study’s results suggest that the performance of NNES candidates may vary by their teaching assignment and corresponding edTPA portfolio to complete. The results also revealed that NNES candidates’ perceptions centered on their struggles with the writing requirements for edTPA and their lack of awareness of the assessment’s expectations and its connections to coursework. Both candidates and faculty mentioned the benefits of practice edTPA tasks infused in coursework and content-specific seminars offered during the internship. The use of customized language support such as peer editing and the use of other writing resources was reported by candidates and faculty to be especially beneficial for NNES candidates. The study’s findings serve to inform teacher preparation programs as they strive to improve the edTPA preparation of all candidates, including those whose first language is not English.



Candidate Name: Wendy Lewis
Title: A DESCRIPTIVE CASE STUDY OF ELEMENTARY MATHEMATICALLY PROMISING STUDENT INTERACTIONS WITH COGNITIVELY DEMANDING MATH TASKS
 November 03, 2022  12:30 PM
Location: UNC Charlotte, College of Education, Department of Reading & Elementary Education
Abstract:

National mathematics achievement results show elementary students in the United States are not increasing in cognitive ability or critical thinking skills (NAEP, 2019). Furthermore, students who are mathematically promising need more opportunities for cognitively demanding mathematics instruction in order for this increase to occur. Therefore, this descriptive case study focused on the interactions and emergence of Mathematical Practices in seven third grade students with a series of five tasks. The seven third grade students were identified by their teachers as mathematically promising. The tasks used in the two suburban classrooms observations of the study were from the Tools 4 NC teachers framework (Tools 4 Teachers, 2019). Data sources collected included pre- and post-focus group audiotapes, classroom observations via audio and video, field notes, as well as document analysis of student work and a teacher debrief form. Blumer's theory of social constructivism (1969) and Tripathi’s Multiple Reasoning (2008) guided this study.

Findings from the students' interactions with tasks showed the following themes: students used a variety of interpersonal interactions between themselves, the teacher, and visual representations. Students used mathematical writing to justify their reasoning and reflection to communicate their conceptual mathematical understanding. Students grew in their emergence of the Mathematical Practices of perseverance through problem solving, productive struggle, the construction of arguments, and the ability to make connections. These findings indicate the importance of ongoing curriculum development to include differentiated teacher guidance for mathematically promising students. Additionally, the findings of this study will support mathematics teachers and leaders with a student-centered approach to teaching inquiry-based instruction.



Candidate Name: Jie Chang
Title: Asymptotic Normality of Higher Order Turing Formulae
 October 14, 2022  2:00 PM
Location: https://charlotte-edu.zoom.us/j/97032159477
Abstract:

Higher order Turing formulae, denoted as T_r for r ∈ Z+, are a powerful result allowing one to estimate the total probability associated with words from a random piece of writing, which have been observed exactly r times in a random sample. In particular T_r estimates the probability of seeing words not appearing in the sample. To perform statistical inference, e.g., constructing the asymptotic confidence intervals, the asymptotic properties of the higher Turing formulae need to be studied.
In this thesis we extend the validity of the asymptotic normality beyond the previously proven cases by establishing a sufficient and necessary condition for the asymptotic normality of higher order Turing formulae when the underlying distribution is both fixed and changing. We also conduct simulation studies with the complete works of William Shakespeare and data generated from different underlying distributions to check the finite sample performance of the derived asymptotic confidence interval.
Based on our theoretical results we also developed two methodologies for authorship detection with real twitter data analysis.



Candidate Name: Akintonde Abbas
Title: Realizing Combined Value Streams from Customer-Side Resources
 October 05, 2022  2:30 PM
Location: EPIC 1332
Abstract:

The importance of flexible customer-side resources in transitioning to a clean energy future is becoming increasingly apparent. Flexible customer-side resources can resolve most issues associated with intelligent and low carbon power grids and, in the process, unlock new value streams for both resource owners and load-serving entities (LSEs) with access to those resources. However, most LSEs with access to numerous flexible customer-side resources often use them for single applications when these resources can provide multiple value streams simultaneously. This dissertation focuses on developing models and frameworks to help LSEs simultaneously capture multiple value streams from customer-side resources within their jurisdiction.

Firstly, a stochastic equivalent battery model (EBM) that provides a simple yet accurate representation of the overall power consumption flexibility associated with a commercial building is proposed. The proposed stochastic EBM combines model-based functional simulations and optimization techniques to quantify the overall flexibility of a commercial building with flexible resources such as heating, ventilation, and air-conditioning (HVAC), electric water heater (EWH), battery, and electric vehicle charging stations. Illustrative case studies showcasing how the proposed model fits into complex resource scheduling problems whose objectives either maximize or minimize some value reflecting the LSE’s intended outcomes are also considered.

Secondly, a stochastic optimization framework is proposed to help an LSE capture value streams involving bulk power system support services from its residential customer-side resources. The specific value streams of interest are energy arbitrage, peak shaving, and market-based frequency regulation, while the customer-side resources are residential HVACs, EWHs, and behind-the-meter (BTM) storage. A resource type-centric clustering method is employed. The proposed framework contains
two parts. The first part involves a day-ahead resource scheduling problem that captures uncertainties in energy prices, regulation prices, and frequency regulation signals. A voltage sensitivity matrix-based approach is proposed to capture the impacts of resource control actions on system voltages. The second part includes
two real-time resource dispatch algorithms capable of eliciting fast responses from the resources to frequency regulation signals from the market operator with minimal voltage violations. The scheduling model and dispatch algorithms are evaluated using a HELICS-based co-simulation platform and real-world market data from New York Independent System Operator (NYISO).

Thirdly, a stochastic optimization framework is proposed to help an LSE capture multiple value streams focused on distribution system operations from its residential customer-side resources. The value streams of interest are peak shaving, energy arbitrage, ramp rate reduction, loss reduction, and voltage management. The framework captures the impact of third-party aggregators on the LSE’s network and includes two dispatch algorithms - decision rule-based dispatch and optimal real-time dispatch.

Finally, a framework to help LSEs compensate owners of customer-side resources for multiple value streams is proposed. The compensation sharing approach classifies the LSE’s realized value into three categories - additive, super-additive and subadditive. The appropriate compensation-sharing mechanism is then defined for each value category. A special component of the compensation sharing mechanism that provides additional social benefits, specifically credit rating improvement, for low and medium-income flexible resource owners is also proposed.



Candidate Name: Kala S. Wilson
Title: The Intersection of Health Informatics and Disparities: Understanding How Data Promotes Health Equity
 October 10, 2022  9:00 AM
Location: https://charlotte-edu.zoom.us/j/96710653514?pwd=d01ubUJPYTdlZTQ1VTdzVHFLV2RjZz09; Passcode: 101022
Abstract:

In this collection of manuscripts, I develop a deeper understanding and insight into how the Coronavirus Disease 2019 (COVID-19) pandemic and subsequent transition to telehealth impacted 1) clinical electronic health record (EHR) data quality and data entry patterns, 2) provider perceptions of the EHR’s influence on care delivery, and 3) patient perceptions on barriers related to pandemic-induced telemedicine.

The COVID-19 public health crisis has disproportionately affected individuals and populations historically marginalized in healthcare and public health, including racial and ethnic minorities and individuals with low-income status. The COVID-19 pandemic has drawn new attention to and compounded the existing health and digital disparities in healthcare, with Black Americans being almost 4 times more likely to die from the virus than White Americans. Racial and ethnic health disparities have been historically unwavering and persistent within the United States. Furthermore, this crisis has ignited rapid implementation of digital healthcare solutions such as virtual healthcare (telehealth and telemedicine capabilities) and health information technology (HIT) accessed via mobile applications or online platforms. When assessing HIT’s effectiveness, efficiency, quality, safety, and equity, it is important to consider the reciprocal relationship between HIT and the COVID-19 pandemic. This is of marked significance, considering that virtual care technologies have been shown to exacerbate the digital divide and worsen disparities in a patient’s ability to access high-quality care.

The research in this dissertation is informed by the socio-technical and complex systems perspectives of improved human health via high-quality, safe, HIT-driven care, which maintains two central concepts: 1) multiple levels of influence affect a patient's health outcomes, such as care quality, costs, and patient safety; and 2) complex adaptive systems occur when many agents work together within an organization and patterns materialize as the agents adopt, "simple rules" that optimize outcomes, such as the patient experience and the clinical team’s performance. Understanding how these HIT-related behaviors and perceptions multidimensionally affect care delivery is imperative to maximizing the potential benefits of technology and data in healthcare and promoting the need for a concerted effort to ensure safe, high-quality, and equitable care delivery.

Chapter 1 reviewed literature on the relationships between HIT and care quality, patient safety, health equity, biases, and discrimination. In Chapter 2, we assessed the influence of external, societal factors on disparities in data quality and data entry patterns. We found that an external change to healthcare operations – which modifies clinical practice – was correlated with clinical data entry patterns. Also, we found significant differences between departments within the healthcare organization, suggesting there were data entry differences based on distinct care goals housed within different units. These findings underscore some of the conclusions found in Chapter 3 where we determined the multidimensional relationship between HIT processes and patient safety and quality by exploring how healthcare provider demographic and health system-related characteristics were associated with their perception of the EHR’s impact on care delivery.

Perception disparities were present by providers based on sex, age, race, ethnicity, board certification, telemedicine utilization, and years of EHR experience. The results from this research are striking - we uncovered that providers using the EHR and telemedicine were roughly 20 times more likely to perceive the EHR as beneficial for patient safety (OR=20.25; p<0.001), compared to approximately only 4 times more likely for care quality (OR=4.48; p<0.05). Despite providers reporting that they found the EHR more beneficial for patient safety than care quality – we found conflicting practical evidence when assessing patient perceptions of telemedicine barriers and their reported outcomes.

Chapter 4 assessed the effect of demographic and healthcare-related factors on patient perceptions of telemedicine barriers. We found that 76% of patients reported facing at least one telemedicine barrier, and 66% reported experiencing a medical error via telemedicine during the pandemic. Similarly, we uncovered patients were more likely to report experiencing a telemedicine barrier if they utilized the patient-facing EHR (OR=27.72), had been diagnosed with one to two chronic conditions (OR=10.06) and experienced a medical error (OR=1.22). Interestingly, patients were less likely to report experiencing a telemedicine barrier if they identified as Black (OR=0.10; p<0.001), Female (OR=0.06; p<0.05) and reported three to four diagnosed chronic conditions (OR=0.10; p<0.01). These findings align with prior literature indicating the historically pervasive inequities and disparities amongst these subpopulations. This has been shown to lead to less patient engagement and activation, specifically in Black women, as well as those considered as “super-utilizers” of the healthcare system, often due to complex physical, behavioral, and social needs.

Collectively, these studies advance our understanding of how external factors such as COVID-19, modified workflows, demographic, health system, and healthcare-related characteristics impact health information technology and data perceptions and behaviors. Our findings suggest that these perceptions influence diagnostic EHR data entry, technological utilization, digital care barriers, and corresponding patient outcomes. This dissertation contributes to the public health and healthcare literature by providing practical implications for health systems, clinicians, care teams, and patients. Especially those who interact with technology and data in healthcare settings that affect the efficiency, safety, quality, and equity of care delivery, as well as generated clinical and population health data. Our findings underscore the need for further analysis to understand the interactions between the environment, processes, workflows, technological designs, patients, and the core operative nature of the system itself. Health administrators, policymakers, and researchers must acknowledge that technology and data can act as a roadblock to achieving health equity throughout this nation’s healthcare systems if human and information technology systems continue to co-exist but not co-evolve concurrently.

In policy and practice, we must pull back the curtain and recognize and address the many forms of coded inequity that is present throughout our healthcare systems by becoming more aware of the social dimensions of technology that generate dominant and discriminatory structures encoded in apps, algorithms, and payment data used in health and healthcare.



Candidate Name: Katherine Mauzy
Title: Investigating the Influence of Literacy Coaching as Embedded PD on Teacher Instruction
 October 07, 2022  11:00 AM
Location: Zoom
Abstract:

Improving elementary student reading achievement has been a well-defined goal of many federal and state educational initiatives over the last several decades. To that end, vast amounts of resources have been funneled into professional development for literacy teachers to solve the problem of students who are unable to read proficiently on grade level through a focus on improving teacher literacy instruction. The employment of literacy coaches into elementary schools has been an embedded professional development strategy implemented to combat these low literacy achievement rates across the country. However, the effectiveness of literacy coaches in positively influencing teacher instruction has not been rigorously investigated and many questions still remain about their true impact of improving student reading achievement.

This qualitative case study examined how teachers perceive a change in their instructional beliefs and practices through intervention with a literacy coach and which factors of coaching teachers report as most influential on their classroom practice. The implications of coach/teacher interplay were investigated through the entrenched professional development experiences of three elementary literacy teachers and their school-based literacy coach to determine the specific strategies, conversations, and interventions that brought about a possible change in teacher beliefs and practices relating to the teaching of literacy in their classrooms. A within-case analysis revealed changes in teacher beliefs and practices surrounding small group reading activities including phonics integration as well as changes in whole group phonemic awareness (PA) instruction. A cross-case analysis uncovered meaningful themes highlighting coach proximity to practice including shared responsibility, the coach as a sounding board, and scaffolding coaching cycles. Detailed results combined with recommendations for future research work to advance the field in an effort to develop an effective model of literacy coaching implementation.



Candidate Name: James Johnson
Title: ON WHOLE GENOME CLASSIFIER PERFORMANCE IN RELATION TO 16S CLASSIFIERS
 September 30, 2022  10:00 AM
Location: Zoom: https://charlotte-edu.zoom.us/j/97413134504
Abstract:

There is little consensus in the literature as to which approach for classification of Whole Genome Shotgun (WGS) sequences is most accurate. In this defense, two of the most popular classification algorithms, Kraken2 and Metaphlan2, were examined using four publicly available datasets. Surprisingly, Kraken2 reported not only more taxa but many more taxa that were significantly associated with metadata. By comparing the Spearman correlation coefficients of each taxa in the dataset against more abundant taxa, it was found that Kraken2, but not Metaphlan2, showed a consistent pattern of classifying low abundance taxa that were highly correlated with the more abundant taxa. Neither Metaphlan2, nor 16S sequences that were available for two of four datasets, showed this pattern. These results suggest that Kraken2 consistently misclassified high abundance taxa into the same erroneous low abundance taxa. These “phantom” taxa have a similar pattern of inference as the high abundance source. Because of the ever-increasing sequencing depths of modern WGS cohorts, these “phantom” taxa will appear statistically significant in statistical models even with a low classification error rate from Kraken2. These findings suggest a novel metric for evaluating classifier accuracy.



Candidate Name: Andréa Kaniuka
Title: Mental health promotion and suicide prevention among sexually and gender diverse adults
 October 14, 2022  3:00 PM
Location: Zoom Meeting ID: 992 6660 5096 | Passcode: 591474
Abstract:

Sexual and gender minority (SGM; e.g., lesbian, gay, bisexual, transgender) individuals are recognized as a health disparity population due to the undue burden of mental and physical health disorders among this population. The National Institutes of Health Sexual and Gender Minority Research Office (NIH-SGMRO) generated a social-ecological research framework for SGM health disparity research, articulating need for further research in the areas of (a) minority stress, (b) resilience, (c) violence and discrimination, and (d) intersecting identities. Informed by this research framework, the current dissertation contains three studies attending to these four research areas. Study one is a grounded theory of SGM suicide; 30 interviews with SGM adults in the United States lead to the co-construction of the SGM Suicide Risk and Protection (SuRAP) which outlines the impact of minority stress on suicide outcomes for SGM adults. Study two is a psychometric evaluation of the Brief Resilience Scale (BRS) among a sample of alternative sexuality community members (e.g., persons engaging in non-monogamy and kink), validating use of the BRS in future resilience-based research among this population. Study three is an examination of the mental health outcomes of sexual harassment, using a Psychological Mediation Framework to assess the ways in which social support, emotion regulation, and internalized minority stress explain the sexual harassment-mental health linkage among trauma-exposed sexual minority women. Taken together, findings indicate that therapeutic modalities such as Affirmative Dialectical Behavior Therapy may be of clinical utility.




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