Modern technological advances and trends require new pedagogy and delivery of mapping materials to prepare the future workforce in the disciplines of Science, Technology, Engineering, and Mathematics (STEM) for an increasingly technology-dominated society. The purpose of this study was to develop an Environmental Systems Research Institute (ESRI) Story Map “Soil Forming Factors: Climate” for multiple STEM disciplines based on the design guidelines from the Cognitive Theory of Multimedia Learning (CTML). The study incorporated a two-group post-test only design that utilized multiple methods of analysis, including a quality assessment of the ESRI Story Map using the five principles of effective storytelling, direct quantitative measures of learning (quiz), and surveys of participants’ perceptions (PowerPoint presentation versus ESRI Story Map) using Qualtrics^TM. Survey results were summarized by gender, academic classification (year), and academic major program. The newly developed ESRI Story Map was an effective teaching tool as demonstrated by quiz scores and students’ positive responses. Post-testing scores indicated that ESRI Story Maps were an effective way to learn and were viewed as effective as traditional teaching methods (e.g., PowerPoint). Additionally, students reported feeling comfortable using ESRI Story Maps. These positive responses were consistent by gender, major, and academic classification (year). This research indicates that adding audio may improve the use of this ESRI Story Map for educational concept delivery. Empirical studies such as this that include multifaceted quantitative and qualitative assessments are critical to understanding the benefits of new types of multimedia learning in an educational setting.

Geographic information systems (GIS) have become essential tools in the public health domain, especially when it comes to monitoring and surveillance of disease. The purpose of this article is to describe and explore the benefits of using GIS to improve public health emergency response during a global pandemic and, in particular, how to effectively optimize the allocation of public health resources in a rural setting using a data‐driven approach that considers the multifactorial demand for new COVID‐19 testing sites. Herein, the authors present their interprofessional project as an example of such efforts to inform applications for practice. The team developed a GIS‐based multicriteria decision analysis model for use by decision‐makers and public health experts in similar future planning and response scenarios. Focus is placed on rural characteristics (e.g., accessibility), vulnerable populations, and daily changing conditions (e.g., COVID‐19 daily case fluctuations) that create additional challenges for public health agencies and policymakers.

Introduction: The purpose of this study was to characterize the at-risk diabetes and prediabetes patient population visiting emergency department (ED) and urgent care (UC) centers in upstate South Carolina. Methods: We conducted this retrospective study at the largest non-profit healthcare system in South Carolina, using electronic health record (EHR) data of patients who had an ED or UC visit between February 2, 2016-July 31, 2018. Key variables including International Classification of Diseases, 10th Revision codes, laboratory test results, family history, medication, and demographic characteristics were used to classify the patients as healthy, having prediabetes, having diabetes, being at-risk for prediabetes, or being at-risk for diabetes. Patients who were known to have diabetes were classified further as having controlled diabetes, management challenged, or uncontrolled diabetes. Population analysis was stratified by the patient's annual number of ED/UC visits. Results: The risk stratification revealed 4.58% unique patients with unrecognized diabetes and 10.34% of the known patients with diabetes considered to be suboptimaly controlled. Patients identified as diabetes management challenged had more ED/UC visits. Of note, 33.95% of the patients had unrecognized prediabetes/diabetes risk factors identified during their ED/UC with 87.95% having some form of healthcare insurance. Conclusion: This study supports the idea that a single ED/UC unscheduled visit can identify individuals with unrecognized diabetes and an at-risk prediabetes population using EHR data. A patient's ED/ UC visit, regardless of their primary reason for seeking care, may be an opportunity to provide early identification and diabetes disease management enrollment to augment the medical care of our community.

Approximately 11.6% of Americans have diabetes and South Carolina has one of the highest rates of adults with diabetes. Diabetes self-management programs have been observed to be effective in promoting weight loss and improving diabetes knowledge and self-care behaviors. The ability to keep vulnerable individuals in these programs is critical to helping the growing diabetic population. Utilizing machine learning is gaining popularity in healthcare settings. The objective of this study is to assess the effectiveness of several machine learning methods in predicting attrition from a diabetes self-management program, utilizing participant demographics and various evaluation measures. Data were collected from participants enrolled in Health Extension for Diabetes (HED). Descriptive statistics were used to examine HED participant demographics, while Mann-Whitney U tests and chi-square tests were used to examine relationships between demographics and pre-program evaluation measures. Through the various analyses, health-related measures – specifically the SF-12 quality of life scores, Distressed Communities Index (DCI) score, along with demographic factors (race, age, height, and educational attainment), and spatial variables (drive time to the nearest grocery store) emerged as influential predictors of attrition. However, the machine learning models showed poor overall performance, with AUC values ranging from 0.53 – 0.64 and F-1 scores between 0.19 – 0.36, indicating low predictive power. Among the models tested, XGBoost with downsampling yielded the highest AUC value (0.64) and a slightly higher F-1 score (0.36). To enhance model interpretability, SHAP (SHapley Additive exPlanations) was applied. While these models are not suitable for accurately predicting individual attrition risk in diabetes self-management programs, they identify potential factors influencing dropout rates. These findings underscore the difficulty for models to accurately predict health behavior outcomes, highlighting the need for future research to improve predictive modeling to better support patient engagement and retention.

The soil-biosphere nexus is a critical component embedded in the concepts of soil security and ecosystem services (ES) and is directly linked to several global challenges identified by the United Nations (UN). Although soil security has been proposed as a policy framework, a major problem is that there are no standard procedures to assess the five dimensions of soil security: capability, condition, capital, connectivity, and codification in relation to the soil-biosphere nexus. This study proposes a land cover change matrix, disaggregated by soil type (“biophysical soil security matrix”), as a tool to evaluate the biophysical soil security continuum and its temporary changes, integrated with ES valuation. The matrix was tested using the state of Minnesota (MN) as a case study. Although the dominant soil orders in MN possess high natural capability, widespread human-caused land degradation has dramatically lowered their actual biophysical condition and has fueled massive soil decarbonization. Historic land degradation in MN due to human activity totaled 98,516 km² through 2024, with nearly 480 km² of anthropogenically degraded land created between 2001 and 2024. Based on carbon emissions alone, we estimate that historic land degradation in MN has resulted in total social costs of nearly $50B (U.S. dollars, B = billion = 10⁹) through 2024, with about 10% of this total social cost being realized between 2001 and 2024. Trends like this threaten soil security by directly eroding the soil’s capacity to sustain ES. Based on the analyses and results, this study recommends conducting both soil-centric and human-centric analyses of soil security to ensure the sustainable use of soil.