Dissertation Defense Announcements

Labor dystocia, a term used to describe slowly progressing labor, is the most common reason for cesarean delivery. Despite global efforts to establish improved practice guidelines over the past decade, there is significant debate in the literature about how to manage labor dystocia when it occurs. The present study aims to illuminate 1) the decision-making processes surrounding labor dystocia, which previous literature suggests are complex and involve multiple stakeholders, and 2) the factors clinicians consider as part of these decisions that may contribute to whether a cesarean delivery is ultimately performed. These questions were approached qualitatively using informed constructivist grounded theory methodology. Informants were obstetricians, family medicine physicians, midwives, and labor and delivery nurses in current practice in metropolitan North Carolina hospitals. The primary researcher conducted semi-structured interviews that included a graphic elicitation diagramming exercise and collected sociodemographic data via an online survey. Several methodological strategies bolstered the study’s rigor and trustworthiness. The data revealed four common pathways through which decisions are made in the context of labor dystocia. Additionally, a Social-Ecological Model of Intrapartum Decision-Making is proposed that represents influential factors on decision-making processes at the level of the individual, patient-clinician, immediate social context, care team, maternity center/hospital setting, and broader macrosystem. Findings advance our understanding of how decisions are reached during a uniquely challenging medical experience and may lead to improvements in equitable, high-quality, labor and delivery care.

In this work, the in vitro characterization profiles of delivery vehicles, specifically polyamidoamine (PAMAM) dendrimers, are assessed to investigate their impact on pre-established immune responses to immunostimulatory and immunoquiescent nucleic acid nanoparticles (NANPs). Isolated human peripheral blood mononuclear cells (PBMCs) were used as the universal model system for these investigations, providing detailed understanding of the impact delivery vehicles play on NANP recognition. Additionally, to further identify mechanisms of immune recognition of these novel formulations, several engineered reporter cell lines were employed to understand the involvement of pattern recognition receptors, relevant to nucleic acid detections in human cells.
Furthermore, we explore the design and in vitro assessment of conditionally activated reconfigurable nucleic acid nanoparticles (recNANPs). By further investigating dynamic recNANPs and their interactions with delivery vehicles and the immune system, we aim to gain deeper insights into these systems. This innovative platform will enable the development of refined design principles for therapeutic systems incorporating NANPs, allowing for the creation of more precise and optimized options.

This dissertation enhances the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by integrating information privacy concerns and examining their influence on adopting web-based healthcare portals. Through a survey of 298 U.S. residents using healthcare technologies, the study investigates the interplay between UTAUT2 predictors—Performance Expectancy, Effort Expectancy, Facilitating Conditions, Habit, Social Influence, and Hedonic Motivation—and the intention to use these technologies while assessing how privacy concerns modulate these relationships. Regression analysis highlights the positive impact of Performance Expectancy, Effort Expectancy, and Habit on adoption intent, with privacy concerns significantly moderating the relationship between Effort Expectancy and usage intention.
The research enriches the UTAUT2 model by showcasing the pivotal role of privacy concerns, thus advancing theoretical understanding and enhancing model predictability in the context of healthcare technology. Practically, it offers insights for practitioners and policymakers on addressing privacy concerns to improve technology adoption. This synthesis of privacy concerns within the technology acceptance framework paves the way for targeted strategies to increase the uptake of healthcare technologies, marking a significant contribution to both academic discourse and practical application.

The Context-Aware Conditional Tabular Generative Adversarial Network (CA-CTGAN) introduces an innovative architecture for the generation of synthetic tabular data, distinguished by effectively incorporating context-specific elements into its generative process. This enables the production of synthetic datasets that not only accurately reflect real-world distributions but are also tailored to specific contexts across a variety of experimental domains, including laboratory, field, natural, and clinical experiments, as well as survey research. In many cases, CA-CTGAN can generate data suitable for research purposes, potentially reducing or eliminating the need for certain real-world experiments. By utilizing Transfer Learning the model effectively identifies and exploits complex semantic relationships within the data to ensure the implementation of rigorous contextual requirements and maintains high semantic integrity. Furthermore, a novel auxiliary classifier is implemented, which includes entity embedding and multi-class multi-label capabilities, enabling the creation of enhanced datasets that strictly adhere to the specified contextual requirements. These contributions position CA-CTGAN as a remarkably versatile and efficient tool across multiple scientific disciplines. Its ability to generate high-quality, contextually relevant synthetic data not only streamlines research processes and reduces associated costs but also addresses ethical concerns in sensitive studies. Consequently, CA-CTGAN emerges as an essential resource for researchers, facilitating more ethical, cost-effective, and data-informed experimental design and decision-making.