Science for Starters is a student-led outreach initiative that provides weekly after-school STEM programming for elementary grade students at the Como Community Center in a historically underserved neighborhood. Supported by the EPIC (Experiential Projects to Impact the Community) grant, the program aims to address gaps in grade-level STEM skills and limited access to hands-on learning opportunities. TCU undergraduate volunteers lead each session, which includes relationship-building activities, a brief introduction to a STEM concept, and a hands-on, inquiry-based activity that encourages problem-solving and collaboration. Topics explored include chemistry, physics, space exploration, engineering, and the human body. Through these experiences, the program fosters curiosity in STEM while promoting mentorship, leadership development among undergraduate volunteers, and sustainable STEM enrichment within the Como community.

Unhoused individuals with diabetes frequently present to the Beautiful Feet Ministries Medical Clinic with preventable foot complications due to limited access to foot care supplies and limited education on preventive practices. This project addresses these gaps through a combined resource distribution and educational intervention model. Free foot care kits, patient-friendly educational materials, and an instructional video were developed to support preventive foot care and improve recognition of warning signs. Awareness workshops further expand outreach and encourage clinic-based foot screenings. By integrating accessible resources with targeted education, this initiative aims to empower unhoused individuals with diabetes to take a proactive role in their foot health and reduce avoidable complications.

Quantifying the Reach of Social Determinant–Focused Supplemental Benefits in Medicare Advantage: A Health Informatics Approach
Medicare Advantage (MA) plans now offer supplemental benefits that go beyond traditional medical coverage. These include things like transportation to appointments, meal delivery, housing support, utility assistance, and pest control, all of which target the social determinants of health (SDOH) that affect patient outcomes well before a doctor visit ever happens. Federal policy, particularly through the expansion of Special Supplemental Benefits for the Chronically Ill (SSBCI), has given plans more flexibility to offer these services. But offering a benefit and actually getting it to the people who need it are two different things. I wanted to find out how many plans are really providing these benefits, and how many beneficiaries are actually enrolled in them.
Using SAS, I combined multiple CMS administrative datasets, including Plan Benefit Package (PBP) data and MA Enrollment by Plan files from the Centers for Medicare & Medicaid Services, into one analytic dataset. I linked plan-level benefit indicators to enrollment counts so I could estimate both the proportion of MA plans offering specific SDOH-related benefits and the percentage of beneficiaries enrolled in those plans.
What I found early on is a clear gap. Plans may list SDOH benefits on paper, but enrollment in those plans varies sharply depending on the benefit type. That disconnect between what is offered and who it actually reaches matters, because it tells us that expanding policy alone does not guarantee equity. This project shows that publicly available CMS data, when properly organized and integrated through health informatics methods, can expose these gaps and move the conversation from policy language toward something measurable.

Background: It is estimated that close to 50% of Americans experience stress daily. Research has documented that Latinos report higher levels of stress than other ethnicities. Stress is a normal occurrence and defined as the body and brain's natural, automatic response to any demand, challenge, or perceived threat. There are many factors that can impact stress, including sleep, diet, and exercise which are modifiable risk factors. These factors may affect ethnic groups in different ways. Purpose: The purpose of this integrative literature review is to explore the impact of exercise, diet, and sleep on the stress levels or perceptions of stress in the Hispanic and Non-Hispanic population. Methods: Databases used for this review included: CINAHL, EMBASE, PsycInfo, PubMed, Web of Science, and Scopus. The inclusion criteria were randomized control trials, mixed-method studies, quantitative and qualitative studies, systematic reviews, individuals age 18 or greater, Hispanic and Non-Hispanic populations. The articles reviewed included the years 2006-2026 utilizing the PRISMA extraction system. Conclusion: The findings should help researchers identify strategies to mitigate negative responses to these factors. Based on the increasing Hispanic demographic in the US, future research should address the inclusion of this population to address health disparities and gaps and enhance culturally competent interventions

Key words: exercise, diet, sleep, stress, Hispanic, Non-Hispanic, perceptions, diet quality

Physicians face increasing difficulty accessing relevant clinical evidence due to time constraints and the fragmentation of biomedical literature across multiple databases. Existing search platforms often require separate queries and may prioritize a single source, limiting the breadth and efficiency of evidence retrieval. This project aimed to develop and evaluate an artificial intelligence (AI)-based system designed to aggregate and prioritize clinical information from multiple open-access medical databases.
A multi-source retrieval tool was developed that integrates results from PubMed, Semantic Scholar, Directory of Open Access Journals (DOAJ), CORE, the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), PLOS, and the National Network of Libraries of Medicine (NNLM). The system uses AI-assisted query processing and a source-weighting strategy to prioritize databases based on relevance to the query topic. Design decisions emphasized accessibility, ethical use of open-access content, and integration with clinical workflows.
Preliminary testing using representative clinical queries demonstrated the system’s ability to retrieve evidence from multiple complementary sources, increasing evidence diversity compared to single-database searches. Informal physician feedback highlighted the potential value of integrated retrieval for improving search efficiency and supporting evidence-based decision-making.
This work represents an early-stage clinical informatics approach to addressing information overload in healthcare. Future work will include structured usability testing with physicians, refinement of source prioritization algorithms, and evaluation of time savings and clinical relevance. AI-driven evidence aggregation tools may support more efficient clinical decision-making and improve access to high-quality medical information.