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Stressed young adults are at greater risk of lifetime higher morbidity and mortality, highlighting a need for feasible and effective approaches to improve stress management in this population. Mindfulness, defined as intentional awareness of the present with an attitude of nonjudgment, is a promising intervention to improve a host of physical and mental health outcomes. The stress-buffering hypothesis posits that mindfulness may mitigate harmful consequences of chronic stress through top-down modulation of stress perception as well as bottom-up regulation of stress response systems (Creswell & Lindsay, 2014). Further, Lindsay & Creswell’s Monitor & Acceptance Theory (MAT; 2017) proposes that experiential acceptance is a key component of these effects, without which stress reactivity may be exacerbated. This study sought to investigate the stress-buffering mechanisms of mindfulness and replicate prior research supporting MAT using a single session dismantling study comparing regulated breathing control, monitor-only, and monitor + accept conditions in a sample of 33 stressed young adults who completed an in-lab social evaluative stressor. A series of linear hierarchical regressions and multilevel models were utilized to compare condition effects on physiological and psychological stress responsivity and reactivity, respectively. Individual-level predictors (e.g., trait mindfulness, self-compassion) that may moderate stress-buffering effects were also examined, and exploratory qualitative analysis of participants’ perceptions was conducted. No hypotheses were supported by the study’s findings, most likely due to the underpowered sample. Qualitative results further suggest that the study’s active control was potentially equally efficacious to the mindfulness conditions in buffering stress. Future research directions include clarifying the minimal amount of mindfulness practice needed to observe stress-buffering effects as well as investigating how mindfulness is most effectively learned in stressed populations.
Healthcare organizations play a key role in supporting health for a growing population of older adults. With the emergence of electronic health records, routinely collected data can be leveraged to identify vulnerable older adults more easily. Healthcare organizations can employ risk stratification, interventions, population management strategies, and community partnerships to enhance health and care for high-risk populations. Frailty, an internationally recognized indicator of vulnerability associated with numerous adverse outcomes, has received attention as a viable target for intervention as it provides a multidimensional view of an individual’s health status. This dissertation contains three studies that examine how structured data from the electronic medical record might be leveraged to identify older adults with elevated risk of experiencing adverse events. The first study explores the joint association of frailty and neighborhood disadvantage with emergency and inpatient utilization and considers how area-level variables may contribute to recognizing older adults with unmet needs across functional, medical, and social domains. The second and third studies leverage longitudinal frailty measures to explore frailty transitions in a unique healthcare context to inform strategies for the prevention, delay, or even reversal of frailty. The third manuscript considers how rural residence modifies the associations between frailty state transitions and individual-level predisposing and need factors as well as contextual-level predisposing factors.
This dissertation addresses critical aspects of traffic safety, focusing on novel approaches for weather-related crash prediction—a significant concern in the transportation field. It is divided into three interconnected studies: geospatial risk mapping of weather-related crashes, addressing data imbalanced in machine learning for weather-related crash severity analysis, and analytics for future weather-related crash prediction. In the first study, the dissertation advances a novel approach to hotspot mapping by developing a spatio-temporal cube that incorporates both the spatial and temporal dimensions of crash data, providing a dynamic and comprehensive analysis of crash hotspots. In the second study, the dissertation tackles the challenge of imbalanced data, which can bias machine learning model outputs, making them less adept at predicting crash severity. By extending methods such as Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), the dissertation evaluates the effectiveness of these methods in datasets with a prevalence of nominal predictors, aiming to enhance the predictive accuracy of machine learning models for crash severity. Lastly, the dissertation proposes the use of Spatially Ensembled ConvLSTM algorithm for predicting a weather-related traffic crash. This approach aims address the limitations of traditional predictive models by leveraging the ability of LSTMs to retain relevant information over extended time frames across different heterogenous spaces. The proposed technique was compared with existing methods to test if it outperform conventional predictive models and the standard ConvLSTM in accuracy.
Domestic violence (DV) is widely recognized as a significant problem with detrimental impacts on the mental, physical, and socio-economic well-being of individuals, families, and the broader community. Despite various resources designated to support survivors, they may not be easily accessible or readily available. More importantly, DV survivors are often reluctant to divulge their experiences with others and may even refrain from seeking assistance because of social, emotional, privacy, and cultural concerns. As the immediate response to DV is crucial to survivors' physical and psychological well-being, they need prompt and non-blaming first responders.
Technological advancement, particularly in automated Conversational Agents (CAs), is progressing rapidly. CAs are gaining attention as a promising tool for providing counseling and support, addressing the above-mentioned challenges with survivors using traditional supporting resources. The primary objective of this dissertation research is to design, develop, and evaluate a CA-based solution that assists DV survivors in receiving support, enhancing their awareness, and increasing their access to services. To this end, we first identify the meta requirements and design principles of CA for survivors by interview-ing DV professionals, then design and develop SafeHaven, a CA-based prototype for supporting DV survivors, by following the design principles and meeting the meta requirements, and finally evaluate the effectiveness and user perception of SafeHavan by conducting user experiments.
Our findings suggest that CAs should empathize with survivors' experiences and provide them with meaningful informational, tangible, and emotional support, ensuring their safety as well as maintaining a transparent, private, and trustworthy dialogue. An evaluation of the CA was carried out with 36 participants, including DV survivors, their friends, family, and professionals, evaluating, among other metrics, the emotional, informational, and instrumental support provided. It was discovered that the CA outperformed traditional online searches and ChatGPT in terms of providing emotional, informational, and instrumental support, high information quality, and user trust.
This research identifies meta-requirements and design principles for designing a CA for DV survivors. This is the first research to evaluate the effectiveness of CAs in assisting individuals with DV, providing tailored, context-sensitive assistance that is superior to the capabilities of traditional search engines and general AI platforms like ChatGPT. In a broader sense, our results will be instrumental in guiding the development of future CA-driven support systems for DV survivors. This dissertation emphasizes the transformative potential of CAs for survivors of DV, as well as significant implications for CA developers, DV organizations, and support groups, proposing innovative strategies for enhancing anonymity, accessibility, and support for survivors.
Racially minoritized students (RMS) face substantial disparities in college persistence and completion rates (Museus & Saelua, 2017). In particular, Black student enrollment at public two-year or community colleges has declined significantly, dipping below 13% in 2020, while for-profit institutions have maintained enrollment of Black students at roughly 28% over a 10-year period (AACC, 2023). Because community colleges have a reputation for being low-cost, high-quality institutions with more than 60 % of its graduates free of student loan debt (AACC, 2019), proper attention must be given and action taken to identify and address the needs of RMS in the community college settings to increase persistence and graduation rates. Sociological research on community colleges highlights the stratified tension between the increased provision of access as open-door institutions against low rates of successful completion (Schudde & Goldrick-Rab, 2014). While culturally relevant education practices have been most successfully implemented in the K-12 space (Ladson-Billings, 1995), an amplified call goes out to responsible community college leaders for the creation of culturally relevant campus environments. Using the culturally relevant leadership practices framework (Jones et al., 2016), this cross-case study explores the roles and practices of presidents and executive leaders within the context of their community colleges to determine how they create spaces for Black student achievement.
Trigger-action Internet of Things (IoT) platforms allow IoT devices to create a chain of interactions to automate network tasks by leveraging functional dependencies between IoT event conditions and actions. When network devices notify their cyber states to the IoT hub by reporting event conditions, the hub utilizes this chain to invoke actions in corresponding IoT devices dictated by user-defined rules. Adversaries exploit this scenario to implement remote injection attacks by maliciously reporting fake event conditions to the hub to force it to command target IoT devices to perform invalid actions violating rule integrity. Security mechanisms in the existing literature either require complete visibility over network events to provide an effective defense against dynamic injection attacks or do not offer real-time security.
In this dissertation, we present three security systems to fill this gap in the literature: 1) IoTMonitor, a Hidden Markov Model (HMM) based security analysis system that extracts optimized attack paths and discovers frequently exploited nodes in the network; 2) IoTWarden, a Deep Reinforcement Learning (DRL) based real-time defense system that allows a defense agent to learn attack behavior by observing the network environment and design an optimal defense policy to counter attacker's actions at runtime, maximizing overall security rewards; 3) IoTHaven, A POMDP-based online defense system to discern optimal defense policy for the partially observable IoT networks.
This dissertation explores the impact of student organizations on the persistence of women in STEM programs at the associate degree level. The findings reveal that participation in SkillsUSA provides students with valuable opportunities for hands-on learning, skill development, and career exploration, all of which contribute to the persistence of the participants in their chosen STEM related field. SkillsUSA offers a range of activities, including competitive events, leadership development, and community service projects, that foster collaboration, communication, and problem-solving skills among students (Maldonado & Jaeger, 2021; Threeton & Pellock, 2016). SkillsUSA can serve as a bridge between classroom instruction and real-world application, allowing students to apply their knowledge in authentic settings and gain practical experience in their chosen fields. Key themes that emerged from the data include the importance of mentorship, peer support, and extracurricular student engagement in shaping student’s academic and career trajectories. The participants of this study expressed gratitude for the guidance and encouragement provided by their SkillsUSA advisors and mentors, as well as the importance of the sense of camaraderie they developed with other women who were working to pursue a STEM career.
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.
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.
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.
