VTechWorks
VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.
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Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
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Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
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More than 50 freely available and openly licensed textbooks are among our most downloaded items.
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Recent Submissions
Understanding health systems thinking in medical education: qualitative interviews with expert clinicians
Norris, Matthew B.; Grohs, Jacob R.; Mutcheson, R. B.; Karp, Natalie; Katz, Andrew; Musick, David; Lane, Heidi; Parker, Sarah; Gonzalo, Jed (2026-01-31)
Background: Health systems science (HSS) education is an increasingly important component of undergraduate medical education. Despite curricular advances, the ways in which clinicians implement health systems science knowledge in everyday clinical practice, health systems thinking, remains understudied. A better understanding of how clinicians engage in health systems thinking to address everyday problems in clinical contexts is needed.
Methods: We conducted semi-structured interviews with 10 expert clinicians experienced in undergraduate medical education, health systems science, and curriculum development to identify components of competent health systems thinking. Interview questions were informed by ecological systems theory and literature on learning professional competencies.
Results: Through interviews with experts, we have come to define health systems thinking (HST) as “an approach to solving problems in healthcare systems that utilizes a deeper understanding of interconnections and behavior of the entire system. As a skill, it coordinates the application of clinical and HSS knowledge and skills toward solving a contextual problem in the healthcare environment.” Clinician comments support the idea that HST is a metacognitive process rather than a specific subset of knowledge domains or affective attributes. This process requires that clinicians understand and navigate pressures on patient care originating from surrounding meso- and macro-systems.
Conclusions: Medical students require more explicit exposure to HSS knowledge being implemented in clinical environments, and varied examples highlighting how meso- and macro-system patterns can impact individual patient care. This metacognitive integration of HSS knowledge into everyday clinical practice is critical for preparing medical students to meet the requirements of the accreditation council for graduate medical education (ACGME) core competencies in residency programs. Health systems thinking requires a method of operational assessment to provide students feedback and highlight targeted interventions for further development.
The effect of flooding on low birthweight and preterm birth: a systematic review and meta-analysis
Mendrinos, Antonia; Loyd, Elly; Jagger, Meredith; Comer, C. C.; Gohlke, Julia M. (2026-03-05)
Abstract Background Numerous studies have examined pregnancy outcomes following flood events, with the majority focusing on two related outcomes: preterm birth (PTB) and low birthweight (LBW). Summarizing the results of these previous studies and determining remaining data gaps is the main objective of this systematic review and meta-analysis. Methods We included publications in English that examined birthweight and/or gestational length related to exposure to floods, or events typically causing flooding (e.g. tropical cyclones). Seven academic databases were searched: CAB Abstracts (CABI), Academic Search Complete and Environment Complete (EBSCOhost), Environmental Science Index & Database (ProQuest), PubMed, Scopus, and Web of Science Core Collection. Searches were updated on February 23, 2025. For inclusion in meta-analyses, quantitative estimates of effect size and variance were required, and quality was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Random effects regression was used for meta-analyses, and results are presented in forest plots, with potential for publication bias assessed in funnel plots and Egger’s test results. Results Overall, data from 34 studies were extracted, and 25 studies across 13 countries were included in meta-analyses. Most studies (N = 18) examined tropical cyclone exposure. Meta-analyses indicate increases in LBW (RR = 1.03, 95% CI: 1.00, 1.05) and PTB (RR = 1.10, 95% CI: 1.00, 1.22). The LBW result was not significantly influenced by quality rating, while the PTB result is non-significant when all studies, regardless of quality rating, were included in the meta-analysis (RR = 1.01, 95% CI: 0.97, 1.05). Additionally, the PTB estimate is strongly influenced by one study with a large and highly significant effect size. Additional sub-analyses suggest no decreasing effect following more recent events (after 2005). Conclusions Results are limited by the range of methods used across studies to estimate exposure to flooding and potential co-exposures related to events that caused the flooding (e.g. wind damage-related health outcomes during tropical cyclones). Regardless, results indicate that adverse pregnancy outcomes may increase following in utero exposure to flood events. Future studies incorporating finer spatiotemporally resolved estimates of exposure to flooding will improve estimates of effect. The study is registered in PROSPERO (CRD42024514540).
Words matter when gangs cyberbang: Predicting imminent urban violence from gang members’ social media posts
Fowler, Sherry L.; Stylianou, Antonis C.; Zhang, Dongsong; Lowry, Paul Benjamin; Mousavi, Reza; Reid, Shannon E. (2026)
The rise in violent crime across major U.S. cities, fueled mainly by gang members using social media to broadcast messages of loss and aggression, poses an urgent challenge. Although prior research has examined gang-affiliated social media content, there remains a crucial gap in identifying which posts serve as credible signals of impending violence. Addressing this gap is essential for enhancing community safety, improving resource allocation, and optimizing law enforcement strategies. This study introduces a novel research model grounded in a contextualized adaptation of signaling theory. The model identifies key indicators of credible signals, such as follower count, specific hashtags, and retweet counts, which correlate with gang-related aggression. Environmental factors, such as temperature, are also examined for their influence on violent crime escalation. Using this contextualized theory, we designed a machine learning model to predict violent crime counts, training it on a dataset of 143,700 gang-affiliated tweets and their accompanying text and metadata. This approach enables automated identification of credible social media signals related to gang violence. The findings contribute to theory and practice by offering new insights into social media credibility and its link to violent crime, and by demonstrating how such signals can be used for prediction. Furthermore, the predictive model provides law enforcement with advanced tools to anticipate crime and inform community-based prevention strategies and policy development.
Fostering Information Disclosure in Telemental Healthcare Settings: How Telehealth Can Mitigate the Deleterious Effects of Stigma
Raimi, Ryan; Lowry, Paul Benjamin; Straub, Detmar (2026)
Insufficient patient disclosure and persistent stigma undermine effective mental health care, a challenge magnified during the COVID-19 pandemic. Telehealth offers a promising avenue to reduce access barriers and improve equity, yet its effectiveness depends on patients’ willingness to disclose sensitive information online. This study develops a middle-range, contextually adapted version of the disclosure processes model (DPM) to explain and predict how stigma and technological features shape online self-disclosure in mental health settings. We conducted a randomized web-based experiment with 309 participants who viewed a video vignette depicting a consultation between a patient and a psychiatrist. The vignette manipulated diagnosis (ADHD vs. schizophrenia) and consultation mode (in-person vs. virtual). Results show that willingness to disclose increases with greater trust in technology, higher perceived social presence, and richer communication media. Initial disclosure goals align with differing levels of technological trust and self-disclosure. However, perceived stigma weakens these positive relationships, reducing patients’ readiness to share sensitive information. The research advances theory by extending the DPM into a context-specific, middle-range information systems framework that integrates stigma and media characteristics in online mental health care. Practically, the findings identify key communication features—such as social presence, richness, and trust in telehealth platforms—that can be calibrated to foster disclosure of stigmatized information. These insights inform the design and implementation of telehealth services that promote open communication and improve treatment engagement in mental health and other stigma-laden domains.
Multivariate Legendre-SNN on Loihi-2 for Time Series Classification and 5G Jamming Detection
Gaurav, Ramashish; Sinha, Sujata; Lin, Chunxiao; Stewart, Terrence C.; Liu, Lingjia; Yi, Yang (IEEE, 2026)
5G-&-Beyond technologies offer the promise of improved speed and bandwidth, ultra low latency, high network reliability, and have the potential to enable new applications and services. It only seems fitting to complement the transformative future of 5G-&-Beyond with the low energy offering of Spiking Neural Networks (SNNs) on neuromorphic chips. In this work, we develop Loihi-2 (Intel’s neuromorphic chip) -compatible versions of our previously proposed Legendre-SNN model for univariate and multivariate Time-Series Classification (TSC), as well as for 5G wireless applications. The Legendre-SNN is a reservoir-based SNN, where, the non-spiking Legendre Delay Network (LDN) is used as a static reservoir, followed by a trainable spiking network. Deploying such an SNN model (mix of non-spiking and spiking components) entirely on Loihi-2 is nontrivial - this is due to the scarcity of related approaches and technical documentations. In this work, we present our approach and the technicalities of implementing the non-spiking LDN on the rarely used “Lakemont core” (embedded on Loihi-2); thereby, adding to the scarce technical documentation to program on-chip Lakemont cores. Thus, our presented approach can be leveraged by other researchers as well - to implement their non-spiking components right on-chip. Our proposed hardware-friendly versions of Legendre-SNN when evaluated on Loihi-2, outperform LSTM-based models (- executed on GPU) on 7 of 24 TSC datasets. Here, we also emphasize on the applications of our Legendre-SNN versions for 5G Jamming Detection on Loihi-2, and complement it with a real-time video demonstration of Jamming Detection (with simulated signals) on our physical Kapoho-Point Single Chip Loihi-2 board, followed by detailed energy-analysis. Overall, this work is directed towards the (comparatively) understudied technical side of neuromorphic computing to enable researchers leverage the Lakemont cores and deploy their SNNs entirely on Loihi-2, with a push towards the cause for neuromorphics in Wireless Communications.