Biology

Environmental Determinants of Disease and Adverse Health Outcomes in Communities of Texas
 

Robert Martinez
Baylor University

In recent years, it has become increasingly clear that many communities in the United States are disproportionately exposed to environmental determinants of disease, resulting in increased adverse outcomes in these local communities. My proposed study aims to examine the potential relationships between social vulnerability and environmental disease incidence, with the hypothesis that disproportionate effects are not occurring among county and regional populations in Texas.  Using probabilistic analysis, we identified differences in the Potential Life Lost Rate (days) within and among the 11 health regions of Texas. Specifically, predicted adverse county health outcomes that were higher than those of other counties in the US (n = 2865) were identified in Regions 1, 2, 11, 9, 4, and 5.  However, Regions 3 and 6, the most populous regions in the state, were identified with lower adverse county health outcomes than the US. Ongoing analyses are examining cancer incidence rates by county and between urban and rural counties within and among regions to identify whether specific locations are disproportionately at risk across the State of Texas.
Keywords: determinants, social vulnerability, probabilistic analysis, disparities

Perceived Instructor Support Drives Student Belonging in Online, Hybrid, and In-Person Biology Courses
 

Roselyn Corona

My research focuses on identifying biology students’ interactions with their instructors in in-person, hybrid, and online courses, and assessing how these interactions influence their sense of belonging. Understanding these experiences is important for developing recommendations to improve instructor support and strengthen student outcomes. This research involves analyzing approximately 1,400 open-ended survey responses using the qualitative analysis software NVivo. The qualitative survey question asks students about their interactions with their instructor and a contextual example to provide details. Through in vivo coding, codes are created based on participants’ responses and categorized as positive, neutral, or negative. Subcodes are then developed using participants’ own words, such as “helpful.” Another code, “Interactions,” includes subcodes to capture the specific context of student-instructor interaction, e.g., in-class, during office hours, etc. Features on the NVivo app can be used to simplify the coding process. For example, the query function allows researchers to compare codes with categories of interest. An analytical memo is continuously updated to document coding decisions, changes, and main patterns. Through examining data co-coded as an interaction type, we will gain understanding of effective interaction strategies for instructors to prioritize.