The purpose of this project was to examine the efficacy of using intracardiac echocardiography (ICE) guidance for Watchman device placement. Transesophageal echocardiology (TEE) with general anesthesia has been the standard for Watchman device placement, but ICE guided placement has become more prevalent.
The methods included a retrospective data analysis of 102 consecutive patients that underwent Watchman placement from May 2024 to October 2024 at a single hospital system. The results were compared for clinical, administrative, and financial endpoints. There were three groups of patients ICE with general anesthesia, ICE with conscious sedation and TEE with general anesthesia.
The significant clinical differences included the transseptal puncture location, ICE access approach, the involvement of interventionalists and imagers, and fluoroscopy time in minutes. From a financial perspective, total hospital charges were also significant. ICE with general anesthesia had the highest patient charges, followed by ICE with conscious sedation then TEE with general anesthesia with less charges.
In conclusion, this project’s findings indicate that Watchman placement with ICE is safe and effective for patients. The financial and administrative differences need further exploration. Difference in experience levels of interventionalists should be considered when examining results in future projects.
Gamification and games-based learning (GBL) have gained increasing attention in higher education as strategies to enhance student engagement, motivation, and learning outcomes. Despite their documented benefits, faculty adoption of gamification in online teaching remains limited. This study examines the factors influencing faculty attitudes toward gamification and GBL in online courses at four-year public universities. Using a quantitative survey-based research design, the study explores the relationships between political climate (PC), anxiety (ANX), institutional gamification readiness (IGR), perceived usefulness of gamification (PUG), perceived ease of use (PEUG), attitudes toward gamification (ATG), subjective norms (SN), and intention to use gamification (IUG).
The study indicates that faculty autonomy and confidence are crucial in gamification adoption. PC and ANX significantly predict PUG, while IGR does not. However, IGR is a key determinant of PEUG, suggesting that structured faculty support reduces implementation barriers. The study also confirms that PEUG fully mediates the relationship between PUG and ATG, demonstrating that faculty are unlikely to develop positive ATG unless they also find it easy to implement. Furthermore, ATG is the strongest predictor of faculty intention to adopt gamification, while SN is not significant.
These results highlight that faculty adoption of gamification is driven more by individual attitudes and perceptions of usefulness than by institutional mandates or peer influence. The implications of these findings suggest that universities seeking to increase gamification adoption should focus on faculty-centered support strategies rather than relying solely on administrative encouragement. Structured professional development programs, instructional design consultations, and financial incentives may enhance faculty confidence and ease the adoption process.
Future research should explore disciplinary differences in gamification adoption, assess its long-term impact on faculty and student outcomes, and investigate how institutional incentives influence faculty engagement with gamified teaching strategies. This study contributes to the growing body of research on technology adoption in higher education, reinforcing the importance of faculty autonomy, structured support, and ease of implementation in the successful integration of gamification and GBL into online learning environments.
This qualitative case study aimed to explore faculty perceptions and design considerations on applying UDL principles to support diverse learning needs for student success. Five faculty participants from a public urban research university were interviewed through semi-structured interviews. Additionally, participants submitted online course artifacts from before and after training for review to illustrate the implementation of UDL principles. Three research questions guided this study: How do faculty learn about UDL guidelines to design online courses? How does faculty knowledge of UDL guidelines influence their online course design? How do faculty perceive challenges in incorporating UDL guidelines into the design of online courses? Participants expressed their thoughts on whether or not their institution should require training and how that training would best be delivered. Participants also discussed applying UDL strategies to their online course designs, including structure and usability. Lastly, participants discussed the challenges of applying UDL principles in online course design including increasing engagement and lack of institutional support. These findings align with the UDL guidelines and the research questions and have implications for online faculty, course designers, and developers.
West Nile Virus (WNV) remains a persistent public health threat in North America, necessitating accurate forecasting models to guide disease surveillance and vector control. This dissertation integrates genomic, epidemiological, meteorological, ecological, and demographic data in an effort to better understand the drivers of WNV outbreaks with the ultimate goal of improving WNV predictions. First, a population genetics analysis of Culex tarsalis, a key WNV vector, identified four distinct locally adapted populations shaped by multiple climatic factors—particularly temperature, precipitation, wind patterns, and daylight length. Second, a national-scale machine learning model incorporating these and other climate variables along with land cover, avian diversity, and human demographics was developed to try and predict WNV case numbers across the continental U.S., but this model ultimately exhibited poor performance due to surveillance data imbalance, inconsistency, and poor temporal granularity. To address the temporal granularity issue, a high-resolution regional model for California was developed, leveraging finer-grained surveillance and environmental data. This model, integrating time-series autoregression and iterative updates, significantly outperformed the national model and showed that the same climate factors driving mosquito adaptation were the most consistent predictors on WNV outbreaks. These findings not only highlight the connection between vector species biology and the diseases they transmit, but also emphasize the need for standardized, higher-quality surveillance data. As climate and ecological shifts continue to influence vector species dynamics and disease spread, data-driven models will be essential for guiding proactive public health interventions.
My dissertation research has two primary objectives. The first is to review the theories of slack resources, organizational legitimacy, agency, upper echelons and stakeholder salience in the context of their impacts on firm ESG participation. The second is to introduce a new model that provides insight into the influences of state political orientation on firm ESG engagement, while considering the potential moderating dynamics of firm size, profitability, socially conscious investors and CEO political ideology. The value of this approach is threefold, it will contribute to ESG literature, provide industry practitioners with empirical evidence to consider when establishing or relocating their firms headquarters and expand the broader knowledge of the influence of politics on firm strategic choices.
This dissertation investigates the interactions between the gut microbiome and hematopoietic stem cell transplant (HCT), with the goal of helping us understand microbial diversity and their functional implications in HCT outcomes. The dissertation is composed with three main projects: 1) analysis of gut microbiota diversity in patients with Graft-versus-Host disease (GVHD), 2) examination of the effect of care given and infusion site on gut microbiome composition and antimicrobial resistance gene diversity, and 3) a focused evaluation of differential abundance analysis (DAA) methods for microbiome data, including DESeq2, edgeR, t-test, and Wilcoxon test.
To achieve these objectives, we have employed high-throughput sequencing techniques, different bioinformatics tools and statistical analysis. The main findings of these projects include observable differences in certain microbial species and calprotectin levels between GVHD statues; site specific variations in microbiome composition for HCT patients; and insights into the performance and suitability of different statistical methods for microbiome data DA analysis.
Our results highlight the importance of considering microbial diversity in HCT and provide insights in improving patient outcomes through microbiome analysis. Additionally, our findings emphasize the importance of carefully selecting the statistical methods in microbiome studies to ensure accurate interpretation of differential abundance analysis results. This dissertation aims to contribute to add knowledge on microbiome research and show potential directions for future studies.
Charge nurses play a critical role in healthcare leadership but often assume the position without structured training, impacting their confidence and effectiveness. This Doctor of Nursing Practice project developed and evaluated an eight-week Charge Nurse Mentorship Program designed to enhance leadership competencies through didactic training, mentorship, and shadowing experiences. Pre- and post-program assessments using the Leadership Efficacy Questionnaire (LEQ) demonstrated significant improvements across all domains: Leader Action Efficacy increased by 63.5% (p =.008), indicating enhanced decision-making confidence; Leader Means Efficacy improved by 33.3% (p =.008), reflecting better resource utilization; and Leader Self-Regulation Efficacy rose by 43.3% (p =.008), signifying greater emotional resilience.
Mentor feedback supported these findings, highlighting the need for continued leadership coaching. This project underscores mentorship as a scalable, cost-effective strategy to strengthen charge nurse readiness, retention, and succession planning. Future research should explore long-term leadership development impacts.
This study examines the barriers and supports that faculty in higher education encounter when integrating educational technology, with a specific focus on gendered differences in these experiences. Using a quantitative survey design, the study explores how faculty members perceive and engage with educational technology, the challenges they face, and the institutional and societal factors that shape their experiences. A total of 75 faculty members from Magnolia University, a large public university, participated in the study.
Drawing upon feminist and critical theory frameworks, this study employs a gendered analysis to assess whether women faculty members experience different challenges or receive different forms of institutional support compared to men faculty. Key areas of investigation include faculty perceptions of time constraints, training opportunities, institutional encouragement, and broader sociopolitical influences on technology use. This study also examines how faculty rank (tenured vs nontenure) interacts with gender in shaping technology adoption patterns.
Generative Artificial Intelligence has the potential to influence organizational strategies and ethical leadership in today's rapidly transforming business landscape. As a socio-technological tool, GenAI plays a crucial role in the digital transformation of businesses, processes, and society, aiming to meet diverse goals from profit-making to economic development. The rapid use of GenAI raises concerns about its impact on employee perceptions of ethical leadership, which can be both positive and negative. This research examines how GenAI use impacts perceptions of ethical leadership and competitive advantage within organizations and further explores how organizational innovativeness and the regulatory environment moderate these relationships. Drawing on survey data from 234 respondents, the study employed a multi-step data-cleaning approach to ensure data quality and utilized Partial Least Squares Structural Equation Modeling for hypothesis testing. Results indicate that GenAI use strongly predicts competitive advantage but does not significantly shape employees’ perceptions of ethical leadership. Interestingly, ethical leadership exhibited a modest yet counterintuitive negative relationship with competitive advantage, hinting at potential trade-offs between ethical considerations and aggressive market positioning. Additionally, the regulatory environment emerged as a significant moderator, amplifying the positive effect of GenAI use on competitive advantage in more regulated settings. In contrast, organizational innovativeness did not meaningfully alter these relationships. By analyzing GenAI’s role in leadership practices, this research aims to enhance understanding of the interplay between GenAI use, ethical leadership, and competitive advantage, contributing to the literature in this field.