26419 research outputs found
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Princess Joy L. Perry
No full textPublicity photo submitted by author/presenter for ODU\u27s Annual Literary Festival 2025.https://digitalcommons.odu.edu/litfest_images/1010/thumbnail.jp
Integrating Self-Determination Theory and Continuous Glucose Monitoring: Promoting Youth Development Among Campers with Type 1 Diabetes
No full textThe purpose of this cohort study was to evaluate participants’ general self-management and experiences of autonomy while attending diabetes camp using quantitative and qualitative data collection. Through a partnership, an outdoor diabetes camp was designed to assist youth with type 1 diabetes (T1D) in their management. The REACH Teen program conducted a week-long summer camp for youth with T1D. The study was designed through Outcome-Focused Programming grounded in Self-Determination Theory (SDT) to meet campers’ needs of autonomy, competence, and relatedness. Campers participated in outdoor activities and diabetes education designed to increase healthy behaviors. Twenty-three campers completed a 24-item pre- and post-camp questionnaire measuring participants’ perceived levels of satisfaction or frustration of their three basic psychological needs. At the conclusion of camp, 21 youth participated in 35-min focus group interviews. Through a paired-sample t-test, all three measures were trending in a positive direction, with relatedness (R) being the closest to significance. Cloud-based biometric data was used to compute the percentage of TIR for the week, during camp hours. The results from the focus group interviews revealed three themes: lack of outside understanding, varying levels of autonomy, and experiences at REACH. Not reporting TIR data outside of camp was a limitation of this study. Diabetes medical specialty camps grounded in SDT can provide an opportunity for campers to internalize healthy behaviors needed to manage their diabetes
Undermining Risk and Technical Communication: Extractive Industry, Cascading Disaster, and the Global Climate Crisis
No full textTechnical and professional communication has a problem with how the concept of risk has been considered alongside extractive technologies. Throughout its history, the practice, teaching, and research of technical and professional communication has been embedded within, complicit with, and indebted to these industries. These industries have also created massive global harm to people and ecosystems, both through accidents as well as the slow violence of pollution and climate change. In response, this book seeks to undermine how technical and professional communication works with risk by reconsidering implications that traverse a greater span of time and geography. It revises the field\u27s risk methodology and encourages future researchers to navigate the scope and scale of their projects. Along with new theoretical framing, the text presents three detailed case studies illustrating how careful consideration of scope and scale can impact how technical and professional communication engages extraction and risk, showcasing to new and experienced technical communication researchers alike how risk communication is about to enter a new era. [Amazon.com]https://digitalcommons.odu.edu/english_books/1073/thumbnail.jp
Annabelle Tometich
No full textPublicity photo submitted by author/presenter for ODU\u27s Annual Literary Festival 2025.https://digitalcommons.odu.edu/litfest_images/1014/thumbnail.jp
Integer-Valued Time Series Model via Copula-Based Bivariate Skellam Distribution
No full textTime series analysis is crucial for modeling and forecasting diverse real-world phenomena. Traditional models typically assume continuous-valued data; however, many applications involve integer-valued series, often including negative integers. This paper introduces an approach that combines copula theory with the bivariate Skellam distribution to handle such integer-valued data effectively. Copulas are widely recognized for capturing complex dependencies among variables. By integrating copulas, our proposed method respects integer constraints while modeling positive, negative, and temporal dependencies accurately. Through simulation and an empirical study on a real-life example, we demonstrate that our class of models performs well. This approach has broad applicability in areas such as finance, epidemiology, and environmental science, where modeling series with integer values, both positive and negative, is essential
Can We Talk? A Correspondence Study to Examine Responsiveness of Physical Educators to Requests for a Phone Call from Parents of Children With Disabilities
Full text linkThe Individuals with Disabilities Education Improvement Act (2004) mandates that parental input be considered when making educational decisions for children with disabilities, including in physical education. However, parents of children with disabilities often report suboptimal communication experiences with physical educators. The purpose of the current study was to examine if the initiation of a parent–physical educator relationship is influenced by whether students have a disability or not. An online message correspondence study methodology was used to detect potential disparities in the responsiveness of a sample of 320 physical educators to electronic message requests for a phone call to discuss physical education service from hypothetical parents of a child with a visual impairment (VI), autism spectrum disorder (ASD), Down syndrome (DS), or no disability. Parents of children with ASD had reduced odds of receiving a positive response as compared to parents of children without disabilities (15.8% vs 26.9% RR = .57). Similarly, parents of children with DS had reduced odds of receiving a positive response as compared to parents of children without disabilities (15.2% vs 26.9%, RR = .59). Strategies to promote parent–physical educator relationships are urgently needed, especially for parents of children with ASD and DS
Interpretable Machine Learning for Bridge-Pier Scour Prediction and Flood Resilience
No full textBridge-pier scour is a leading cause of flood-induced bridge failure, yet practice still lacks transparent, physics-informed tools that link data-driven prediction with design guidance. This study develops an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure. Using the 2014 U.S. Geological Survey Pier-Scour Database (569 laboratory cases), five models: Gradient Boosting, AdaBoost (Tree), XGBoost, Gaussian Process (RBF kernel), and Kernel Ridge (polynomial), were trained and evaluated with K-fold cross-validation. Model performance was evaluated using R², RMSE, and MAE. Gradient Boosting performed best, achieving training and testing R² of 0.99 and 0.96, a near-ideal parity fit, and consistent accuracy across folds. Interpretability is provided by SHAP, whose attributions align with hydraulics; the pier width normal to flow accounts for 70.6% of the total importance in predicting scour depth. Predicted scour is mapped to four scenario envelopes that capture rare, peak, and sustained hydraulic extremes and yield clear design checks for flood resilience. A physics-based imputation scheme for sediment critical velocity and duration of flow is integrated in the framework so that missing inputs are handled in a hydraulically consistent way. The developed models are deployed in an interactive web app, allowing practitioners to obtain code-free scour predictions across all learners. Applied to the Knik River bridge and benchmarked against related work, the framework improves accuracy and provides actionable margins for design verification, maintenance prioritization, retrofit planning, emergency response, and transparent risk communication
Malaika King Albrecht
No full textImage for Virginia Poets Database
Photo by Cari Grindem-Corbetthttps://digitalcommons.odu.edu/vapoets-images/1116/thumbnail.jp