Suspected and Confirmed Norovirus Outbreak Trends in Georgia Health Districts: A Retrospective Review with Observations and Recommendations for Public Health Response Approaches (2017–2024)

Norovirus is a leading cause of acute gastroenteritis in the United States, particularly affecting high-risk congregate settings such as schools, long-term care facilities, and correctional institutions. During the COVID-19 pandemic, shifts in public health operations and infection control practices influenced outbreak dynamics and response strategies. The purpose of this study was to examine trends in both suspected and confirmed norovirus outbreaks across Georgia’s health districts from 2017 to 2024, assess how outbreak characteristics differed before and after the pandemic, and evaluate the impact of remote versus hybrid response strategies on outbreak management. Using a retrospective design, de-identified data from the Georgia State Electronic Notifiable Disease Surveillance System (SendSS) were cleaned and analyzed using a statistical program to identify patterns in seasonality, setting, and intervention approach. Health districts’ response to outbreaks were categorized as using either remote methods or hybrid models (remote plus in-person site visits). Outbreak characteristics, including duration and case counts, were visualized using descriptive tables and figures. Preliminary findings indicated increased norovirus activity following the pandemic, with notable peaks in the 2023-2024 season. Long-term care facilities and schools experienced the most amount of GI outbreaks compared to other settings. Due to limitations in the data—such as missing fields for total number ill, inconsistent closure date reporting, and no standardized field for response type—this study could not draw definitive conclusions about the effectiveness of response strategies. However, literature supports the value of hybrid approaches. Public health departments may benefit from improving outbreak documentation and prioritizing tailored interventions in high-burden settings to strengthen future norovirus response.Master of Public Health (MPH)Public Healt

Informant Discrepancies in Perceptions of Family Functioning: Association with Child Internalizing Symptoms

This study investigated the influence of discrepancies in perceptions of family functioning (parent-child relationship quality, parent-child communication, and family cohesion) on child depressive and anxious symptoms in a diverse sample of families (n mother-child dyads = 175) affected by maternal HIV unbeknownst to the child (M age = 9.61, SD = 2.44). This study found that Latinx children reported worse parent-child communication relative to their mothers than do non- Latinx children. Also, Black children reported better communication relative to their mothers than White children. Using polynomial regression and response surface analyses, this study found that child internalizing symptoms are lower as reported by the child; (1) and marginally by the mother when mother and child congruently report better family cohesion (2) when the mother and child congruently report on communication at either extremely high or low levels, and (3) when the child reports better relationship quality than the mother.Master of Arts (MA)Psycholog

Time Permutation Approaches to Self-Supervised Dynamic Neuroimaging

Functional magnetic resonance imaging (fMRI) captures brain dynamics, offering crucial insights into brain function and disorders. However, its high-dimensional, complex, and noisy nature makes interpretation challenging. Ensuring model interpretability is essential, especially in high-stakes domains like medicine. Addressing this concern requires the development of specialized methods. Another significant challenge is data scarcity, as privacy laws often limit access to clinical data. In such cases, efficient pretraining techniques can be valuable, enabling models to work effectively with limited data while still producing reliable results. To address the challenge of data scarcity, we propose a novel pretraining method called time reversal. Our approach leverages self-supervised learning to train a model on the temporal direction of ICA-preprocessed fMRI data. The pretrained model is then applied to downstream classification tasks for three disorders: schizophrenia, Alzheimer's disease, and autism. Through extensive experiments, we demonstrate that during pretraining, the model effectively learns temporal patterns from a separate dataset. This learned temporal information enhances performance in downstream tasks, as evidenced by improved AUC scores compared to models trained from scratch. Our findings highlight time reversal as a promising approach for capturing essential temporal features and transferring this knowledge to related tasks. To enhance interpretability, we employ model introspection techniques to interpret the proposed pretraining method. We use one of the popular methods (Integrated Gradients) to generate saliency maps that offer post-hoc explanations for pretraining, while Earth Mover’s Distance (EMD) quantifies the temporal dynamics of salient features in the downstream schizophrenia classification task. The saliency maps reveal more concentrated and biologically meaningful salient features along the time axis, aligning with the episodic nature of schizophrenia. We show that, by linking model predictions to meaningful temporal patterns in brain activity, time reversal strengthens the connection between deep learning and clinical relevance. Additionally, it is possible to enhance interpretability by making the intermediate representations of the input more transparent. In most deep learning frameworks, an encoder maps the input to a latent representation, which is then decoded and used for prediction. We develop methods to interpret the latent representations in our self-supervised pretraining task, which focuses on the order of time points. To achieve this, we pretrain a model using time reversal, extract its latent representations, and feed them into a probe (logistic regression) for further analysis. The fMRI data consists of 53 components, which are associated with seven functional brain networks: sensorimotor, visual, sub-cortical, cognitive control, default mode, cerebellar, and auditory. These networks represent connectivity patterns across different brain regions. We first establish a mapping between the fMRI components and the latent features, allowing us to analyze the learned representations in a biologically meaningful way. Using this mapping, we examine the coefficients of the logistic regression probe to determine the contribution of each brain region to schizophrenia classification. This approach provides deeper insights into how specific brain networks influence model predictions, bridging the gap between deep learning and neuroscience.Doctor of Philosophy (PhD)Computer Scienc

Examing how Nurses' Personal Experiences with Mental Illness Relate to Stigma and Discrimination against People with Mental Illness in Rural Northern Uganda.

Introduction: Rural Primary Health Care (PHC) nurses in post-war settings experience mental health problems resulting from traumatic exposures. They also experience burnout and compassion fatigue caring for high number of patients with untreated mental health problems due to cultural practices that promote stigma, and lack of adequate mental health services. Social stigma is society’s negative perceptions of an individual with mental illness, viewing them as socially unacceptable. The purpose of the study is to provide foundational understanding to inform future development of anti-stigma interventions PHC in rural northern Uganda. Research questions included;(1) What percentage of PHC nurses have personal experience with mental health disorders? (2) How do knowledge of and beliefs about mental health relate to mental health attitudes of PHC nurses in rural northern Uganda? (3) Do personal experiences with mental illness moderate the relations between knowledge, beliefs, and mental health related attitudes? (4) Following through with findings from research questions 2 and 3, do the mental health attitudes of nurses, explained by their knowledge and beliefs about mental illness and moderated by their personal experience with mental illness, relate to nurses’ future intended behavior with individuals suffering with mental illness? Methods. To address these research questions, an existing data set of 65 nurse participants that captured stigma measures was used to conduct secondary analysis. In addition to original study variables (attitude, reported future intended behavior, age and sex), new variables of interest were constructed. These included: personal experience with mental illness derived from a 12 item Level of Contact Report (Corrigan et al., 2001), knowledge about common mental disorders and beliefs about mental health both derived from the Mental health Knowledge Schedule (MAKS; Evans-Lacko, et al., 2010). SPSS version 28 was used to generate descriptive statistics and conduct linear regression modelling to answer the research questions. Results: More than a quarter of the study participants (28%) had personal experience with mental illness. Beliefs about mental illness explained significant variance in nurses’ benevolence attitude (β=.755, R2=.135, p=.003), with higher levels of positive beliefs about mental illness being related to higher levels of benevolence attitudes. The interaction of knowledge x beliefs explained significant variance in social restrictiveness attitude (β=-.146, R2=.122, p=.022), with higher levels of knowledge interacting with higher levels of positive beliefs being related to lower levels of social restrictiveness attitudes. Personal experience with mental illness moderated the relationship between combined knowledge of and beliefs about mental illness and social restrictiveness attitudes (β=.070, p Conclusion: To better address nurses’ mental health needs and to counter mental health stigma in rural Uganda, future rigorous studies, which employ sensitive measures of nurses’ personal experience, mental health beliefs, and cultural practices are needed. Future studies should also explore nurses’ mental health needs and innovative ways of addressing their mental health needs in the rural setting.Doctor of Philosophy (PhD)Public Healt

Black School Psychologists’ Experiences Addressing Racial Disciplinary Disproportionality

Racial disciplinary disproportionality (RDD) is a well-documented and long-standing problem in education. Given the historic and systemic nature of racial bias in America, the trend of RDD in education is particularly troubling as it mirrors the practices of the criminal justice system and contributes to the school-to-prison pipeline. Studies have examined the roles and experiences of teachers, administrators, students, and parents as they navigate RDD. However, few studies examine the roles and experiences of school mental health professionals regarding inequitable disciplinary practices. Specifically, the experiences of Black school psychologists as they encounter RDD has been unexplored. Black school psychologists have a unique position and responsibilities regarding RDD given their identities as Black people, mental health professionals, and school personnel. Chapter one of this dissertation employs a scoping review to establish the need for research in this area. Chapter two uses Consensual Qualitative Research (CQR) (Hill & Knox, 2021) to explore how Black school psychologists (n = 13) report experiencing RDD in their work contests. Participants fell into several themes within seven distinct domains. Overall, interviewees identified specific causes and impacts of RDD, discussed district-led and individual-led efforts to address RDD, identified benefits and challenges related to how their Black identity in addressing RDD, and offered suggestions to districts and to future Black school psychologists when addressing RDD. Results of this study lend support for districts to develop and clarify plans to address RDD, practitioners’ use of cultural knowledge when approaching RDD, efforts to mitigate potential stress and burnout among Black school psychologists, and professional advocacy for students who experience RDD.Doctor of Philosophy (PhD)Counseling and Psychological Service

Towards Decentralized Distributed Learning for Dynamic Edge Networks

As Machine Learning (ML) becomes ever more prevalent across disciplinary boundaries and throughout society’s innovations, technological requirements and advancements pull the storage of data and responsibility of computation towards the edge. Federated Learning (FL) began a new wave of algorithms designed for distributed learning. Research in Machine Learning is now progressing even further from distributed to decentralized distributed machine learning. This requires additional considerations such as the limited computational power and communication resources which characterize systems utilizing wireless networks. Current decentralized learning algorithms are not in compliance with these strenuous limitations. We introduce a new algorithm, Peer-to-Peer Critical-Infrastructure-Free Distributed Swarm Learning (PC-DSL), which leverages the characteristics of edge and wireless networks to optimize fully decentralized distributed learning. PC-DSL reduces the maximum number of communications of parameter weight vectors to 1 per agent per step while retaining an 88% testing average on MNIST with 300 training points at 50 agents.M

Le Processus D'acquisition Du Français Comme Seconde Langue : Théories, Défis, Et Approches Pédagogiques.

Ce mémoire a pour objectif d’offrir une analyse des processus cognitifs, sociaux et pédagogiques qui font partie de l'apprentissage du français par des allogènes. Il propose également un aperçu des théories principales qui soutiennent l'acquisition d'une langue étrangère et s'interroge sur les raisons pour lesquelles le français constitue un sujet d'étude pertinent. De surcroit, cet exposé examine certains facteurs qui influencent l'acquisition tels que l'âge, la motivation, les compétences cognitives et les approches pédagogiques employées lors de l’enseignement d’une seconde langue. L’étude s'intéresse encore aux difficultés auxquelles les apprenants sont confrontés pendant leur apprentissage et comment les instructeurs peuvent les aider à surmonter ces difficultés. Enfin, cet exposé suggère que pour faciliter la compréhension et motiver leurs étudiants, les instructeurs doivent aussi ajuster leurs enseignements. Cela créera en eux la confiance en soi et permettra une assimilation facile du français.Master of Arts (MA)World Languages and Culture

Meta-Awareness about Multimodal Composition: Identifying Components of Multimodal Writing Development

In the past decade, there has been a substantial amount of scholarship in the field of multimodal writing with an emphasis on effective assessment models that examine both the multimodal compositional product and the writing process as two equally important assessment criteria. Yet, when it comes to the evaluation of student multimodal writing development, classroom instructors still struggle with how to describe the ways that students grow as multimodal composers, and how to measure writing progress. Carried out in a two-year community college where a student body consists of students with diverse educational backgrounds, this study responds to the needs of FYC instructors who employ principles of multimodal pedagogy by suggesting some indicators of student progress in multimodal writing that can be used to describe and measure the stages of multimodal writing development. In this research, I analyze student digital multimodal compositions to observe evidence of rhetorically-driven rationale in the use of modes and design elements in order to identify some indicators of progress and describe stages of writing development. I conclude that a compositional product can serve as the first indicator of student development and suggest five indicators of progress based on modal relationships: co-occurring, supplementary, complementary, additive, and resistant. I then examine student meta-awareness about rhetorical principles of multimodal composing in written reflections as the second indicator of writing development. I suggest to align three rhetorical concepts – style, rhetorical situation, and rhetorical strategies – with modes, structural elements, and technical features enacted in student reflections to observe evidence of Scraw’s three kinds of metacognitive knowledge: declarative, procedural, and conditional. I recommend that to accurately evaluate the level of student multimodal writing development, the compositional product must be assessed together with the rhetorically-grounded evidence-based reflection. Finally, I observe that three general metacognitive themes can serve as additional indicators of multimodal writing development: positive attitude to multimodal writing, knowledge transfer, and appreciation of multimodal writing for personal growth.Doctor of Philosophy (PhD)Englis

Le code-switching, manifestation de l'hybridité culturelle : une étude de Le Baobab fou de Ken Bugul et Une si longue lettre de Mariama Bâ.

The recurring use of code-switching (CS) in postcolonial African literature requires special attention to unpack its relevance in defining the identity of characters and, by extension, postcolonial individuals. To better understand this fact, this study focused on the use of code-switching in works The Abandoned Baobab by Ken Bugul and So a Long Letter by Mariama Bâ due to the importance of the issue of identity in them. Through a textual and discursive analysis, guided by postcolonial theory, the study demonstrated that CS in both novels reflects the cultural hybridity of the characters, who used it to redefine postcolonial identity.Master of Arts (MA)World Languages and Culture