Presentation Title: Butter Believe It: A Literature Review of Listeria monocytogenes Policy and Impact (No. 1)

Authors: Abigail Meighen, Medical Laboratory Science

Abstract: Listeria monocytogenes is a bacterium found in many processed food products that is able to cause meningitis and spontaneous abortion in pregnant individuals. With such severe complications and unique epidemiology, strong public health measures are key in preventing widespread illness. However, the political precedence of reducing government spending waste manifests as cuts in the capabilities of regulatory departments to fulfill listeriosis outbreak policies. This review analyzes specific food-safety regulations' efficacy and discusses the impacts between overregulation and under-regulation.

Presentation Title: Social Media Use, Relationships, and Mental Health: Among College Students (No. 9)

Authors: Nicholas Burkin* & Meagan Docherty, Department of Psychology

Abstract: With social media becoming more and more prominent in young adults’ lives it raises some concerns and question about how it influences relationships and mental health. In this study, we examine the quantity and quality of social media use and how it is associated with college students’ relationship status and satisfaction, mental health, life satisfaction, and ideals/expectations for romantic partners. For this study, we surveyed 88 college students from Bowling Green State University who were from different socioeconomic statuses, races, genders, and years in college. Results from this study show no significant correlation between social media use and relationship status, although students in a relationship reported slightly more passive social media use. Results also showed that the more a person uses social media, the higher levels of depression, anxiety, and perfectionism they reported. Even though social media use and addiction were not correlated with relationship status, the correlation of negative outcomes and variables raises some concerns.

Presentation Title: Access and Adjustment: The Role of Social Services in Supporting Immigrants Mental Health (No. 2)

Authors: Genevieve Dimmerling, Cierra Banks, & Kinsley Furr - Undergraduate Social Work Major

Abstract: There is a lack of mental health awareness for immigrants in the US. Thus, the purpose of our study was to explore how social services can improve accessibility of mental health resources for immigrants. Our research questions include: (1) How can social service agencies improve accessibility to mental health resources that help immigrants adjust to life in the United States? and (2) How effective are community-based programs in promoting mental health for immigrants?. We used a total of nine articles during the research and found that issues stemmed from socioeconomic disadvantages and lack of understanding of different cultural backgrounds. The limitations are the inability to actually measure how effective mental health resources are and how other external factors can affect the use of mental health resources. We suggest focusing more on community-based approaches and the social workers in this field to promote longevity in the mental health services for immigrants.

Presentation Title: Well-Being of Grandparents Raising Grandchildren: Emotional, Physical, and Family Impacts (No. 4)

Authors: Annika Wright

Abstract: As increasing numbers of grandparents take on the role of primary caregiving for their grandchildren in the United States, understanding the impacts of this shift on their overall well-being has become essential. This study examines how raising grandchildren affects grandparents’ emotional health, physical functioning, daily routines, and gendered caregiving experiences. Eight peer-reviewed studies published between 2002 and 2025 were analyzed using databases such as PsycINFO, EBSCO, and Google Scholar. Findings indicate that grandparent caregivers experience significantly higher levels of stress, depression, and anxiety compared to non-caregiving grandparents, with emotional strain intensifying in long-term or full-time caregiving situations. Physical health concerns, such as chronic illness, fatigue, and functional limitations, are also more prevalent and often increased by the daily demands of childcare. Gender differences emerged, with grandmothers reporting greater emotional and physical burden while grandfathers noted both increased purpose and distinct challenges related to financial strain and hands-on caregiving tasks. These findings highlight the need for micro-, mezzo-, and macro-level social work interventions, including counseling support, caregiver education, community-based programs, and policy reforms that strengthen financial and service resources for kinship caregivers.

Presentation Title: The Paradox of Domestic Violence: Underreporting and the Crisis Cycle (No. 5)

Authors: Annabelle Johnson, Lucinda Furgeson, & A.J. Watercutter

Abstract: This research project examines the complex relationship between the causes of domestic violence (DV) and the underreporting of these incidents. The purpose of this study is to explore how these factors interact and whether one contributes to the other. The study addresses three questions: the main causes of DV, the causes of underreporting, and the relationship between underreporting and DV frequency. Nine peer-reviewed studies from the past ten years were analyzed. Findings show that DV stems from economic strain, power imbalances, generational patterns, and emotional dysfunction. Underreporting results from stigma, fear, dependence, limited resources, and mistrust of authorities, creating a cycle of abuse. Limitations include challenges connecting studies from varying regions and directly linking both topics. Recommendations for social work include culturally competent counseling (micro), promoting awareness in underprivileged communities (mezzo), and advocacy for trauma-informed policy and police training (macro) to strengthen victim support and reporting outcomes.

Presentation Title: From Policy to Practice: Supporting Homeless Students' Success in Schools (No. 7)

Authors: Darci McRae

Abstract: Service providers in educational systems play a vital role in supporting at-risk students, particularly those experiencing homelessness. Despite available services, barriers persist including limited access to transportation, counseling, tutoring, school supplies, and adequate meals. In Wood and Lucas County, Ohio, 2,038 students – approximately 3.1% of total enrollment – experience homelessness. The McKinney-Vento Act (MVA), established in 1987, aims to reduce these barriers, yet its implementation remains inconsistent. This project investigates how service providers in public school districts of Wood and Lucas County, Ohio implement the McKinney-Vento Act resources and interventions for K-12 students experiencing homelessness. This project utilizes 17 resources, comparatively analyzing 7, incorporating a criminal justice perspective for deeper holistic understanding of the topic. Literature reviews suggest students supported by homeless liaisons under the MVA demonstrate improved academic outcomes and higher graduation rates. Interview participants will be selected via convenience sampling based on their involvement in the implementation of the McKinney-Vento Act within Wood and Lucas County schools with provided support letters. Findings are anticipated to benefit social workers’ understanding of areas the act may be lacking in implementation of services as well as addressing the extensive need for this policy to be regularly utilized in schools.

Presentation Title: A Comparison of Human vs. AI-generated Plain Language Communication Material to Improve Infant Vaccination Rates (No. 10)

Authors: Elizabeth A. Nsiah* & Philip John Welch, PhD, MHSA Graduate Coordinator, Department of Public and Allied Health

Abstract: Parental adherence to the CDC vaccination schedule for infants aged 1 to 5 years is declining in northwest Ohio threatening wide-spread, sustained outbreaks of previously controlled infectious diseases such as measles. This study compared human-designed Plain Language Communication (PLC) and AI-generated PLC to determine differences in material creation. With input from Fulton County Health Department staff, we designed a promotional flyer aimed at increasing infant vaccination rates and scored it using the CDC Clear Communication Index to assure alignment with PLC concepts. We then asked the top five AI engines (Copilot, Chat GPT, Claude.ai, Meta AI, and Gemini) to rephrase the flyer language with the goal of increasing adherence to CDC's vaccination schedule for infants. The outputs from these AI platforms were varied and differed in important ways compared to the human version of the flyer. ChatGPT showed the best fidelity to PLC concepts and generated the most human-like language. The other AI platforms tended to provide superfluous information and used a less motivational writing tone. We will present all AI-generated material outputs and the comparisons made to our human-generated flyer.

Presentation Title: Promoting STI Screening among Young Adults using Plain Language Communication (No. 11)

Authors: Dorothy Ansong* & Philip John Welch, PhD, MHSA Graduate Coordinator, Department of Public and Allied Health

Abstract: An estimated 16,304 Ohioans aged 20-24 had Chlamydia in 2024. Many sexually transmitted infections (STI) are asymptomatic, necessitating frequent screening. We examined the influence of Plain Language Communication (PLC) toward sexually transmitted infection (STI) screening. We designed a PLC-based text message using input from Defiance County Health Department staff and scored the message using the CDC Clear Communication Index. Artificial intelligence (AI) was used to ensure active voice. A 35-item Qualtrics survey with text message embedded was sent to young adults in Ohio. Attitudes toward STI screening, intention to screen, and prior health behavior data were collected before and after viewing the text message. Participants provided suggestions to add actionable resources like phone numbers, websites, and physical addresses for STI screening services to the message. Initial findings indicated neutral or mixed attitudes toward STI screening, with doctors and nurses identified as the primary source of health information, followed by social media and family. After viewing the text message, participants reported improved understanding of how to obtain STI screening, improved trust in healthcare professionals, and increased readiness to pursue screening. These findings suggest that PLC messaging supported by human and AI collaboration can positively influence STI screening intentions among young adults.

Presentation Title: Creative Application of Biochemistry Concepts Through AI-Generated Song in Public Health Education (No. 13)

Authors: Kathryn Ayres

Abstract: Students in public health and allied health programs are required to learn and apply complex scientific concepts in a short period of time, particularly in courses that integrate chemistry and biology. New generative AI tools have the potential to support application of these concepts by allowing students to translate scientific knowledge into creative formats that would otherwise require extensive technical training or collaboration across multiple skill areas. In FN 4200—Nutritional Biochemistry, students used ChatGPT to translate amino acid content learned in class into song lyrics and then uploaded those lyrics into the AI music-generation platform SUNO to create original educational songs.  Lyrics were based on amino acid structure and function and incorporated properties such as functional groups, pKa values, polarity, acid–base behavior, and special biochemical cases. Each song included one verse per amino acid and a recurring chorus to reinforce shared concepts. While refinements in prompting were needed to ensure scientific accuracy and clarity, students produced creative outputs that reflected course content. By supporting retention and application of foundational biochemistry, this approach contributes to the public good by strengthening scientific literacy among future public health and allied health professionals. For this presentation, selected songs, lyrics, and assignment materials will be shared to illustrate this instructional approach.

Presentation Title: Comparing Community-Informed and Artificial Intelligence-Generated Revisions to Public Health Text Messaging for HPV Promotion (No. 17)

Authors: Khanh Maitran*, Master of Health Services Administration (MHSA), Department of Public and Allied Health, Katharine Vallerand, PhD, MSW, Department of Public and Allied Health, & Philip John Welch, PhD, MHSA Graduate Coordinator, Department of Public and Allied Health

Abstract: Artificial intelligence (AI) is increasingly used to generate and refine written content, including public health messaging; however, limited research has examined how AI-generated recommendations compare with feedback from community members receiving these messages. This study will analyze differences between community-informed and AI-generated suggestions for improving public health text messages promoting HPV vaccination. A Plain Language Communication (PLC)-based text message encouraging HPV vaccination was designed using input from Putnam County Health Department staff and then scored using the CDC Clear Communication Index to support fidelity to PLC concepts. Ten parents of youths aged 9-17 completed an online survey assessing the clarity of the text messages and provided feedback on how it could be more convincing and actionable. Suggestions included adding resources like phone numbers, websites, and locations for HPV vaccines. The original text message will be entered into five widely used AI systems and prompted to provide suggestions to improve message clarity, persuasiveness, and actionability. AI-generated feedback will be compared with community members' suggestions to identify similarities and differences between human- and AI-informed recommendations. Findings will inform how closely AI feedback aligns with community perspectives and guide thoughtful, responsible use of AI alongside community input in public health messaging.

Presentation Title: Audience Attitudes Toward Health‑Related AI News: An Analysis of Social Media Comments (No. 6)

Authors: Shahla Shahnaz Dyuti*, Ph.D. Student & SM Russel Rabbi, M.A. student, School of Media and Communication

Abstract: This study investigates public attitudes toward health-related Artificial Intelligence (AI) by analyzing audience reactions in comments under news posts shared on social media. Despite AI’s potential to alleviate the global shortage of 4.3 million healthcare workers, public apprehension regarding privacy, bias, and data opacity remains a significant barrier to adoption. While previous research has explored media framing, a critical gap exists concerning the active engagement of U.S. audiences with health-related AI news on social platforms. This research employs a sentiment analysis of comments from the Facebook pages of ten major U.S. news outlets: CNN, Fox News, The New York Times, CBS, The Wall Street Journal, USA Today, The Washington Post, NBC, ABC, and NPR. The study addresses three research questions focused on identifying overall sentiment (positive, negative, or neutral), the prevalence of trust-related concerns, and how these reactions align with specific media frames such as problem definition and moral evaluation. Data collection spans a three-year period (January 2023 to December 2025) to capture shifts in discourse following the rise of generative AI. The findings will provide vital empirical evidence to inform public health communication strategies and policy efforts, ultimately fostering responsible technology adoption in health care settings.

Presentation Title: Capturing Daily Victimization and Academic Engagement During the College Transition: A Dynamic SEM Approach (No. 16)

Authors: Ariana S. Hill* & Eric M. Cooke, PhD, Criminal Justice Program

Abstract: Academic engagement during the first year of college is shaped by multiple contextual and individual factors, with victimization representing a potentially significant challenge during this critical developmental transition. Although prior research has documented associations between victimization and academic engagement, much of this work relies on cross-sectional designs that fail to capture within-person fluctuations in students’ day-to-day experiences. This study examines how daily victimization experiences influence academic engagement during the first semester of college. Sixty-eight first-time first-year students from Bowling Green State University participated in a 21-day daily survey. Academic engagement was measured using the College Academic Self-Efficacy Scale (CASES) and the Higher Education Student Engagement Scale (HESES), while perceived social support was assessed using the Multidimensional Scale of Perceived Social Support (MSPSS). Dynamic structural equation modeling will be used to examine within-person associations between daily victimization and academic engagement over time. We expect higher levels of daily victimization to be associated with lower same-day academic engagement. Findings will advance understanding of how daily experiences of victimization shape academic engagement during a formative period of adjustment to college life.

Presentation Title: Mental Health First Aid and AI: Using AI Tools to Help Train Mental Health First Aiders (No. 15)

Authors: Jarod Mariani* & Christopher Crider-Plonka, Virtual Simulations Program

Abstract: In the field of healthcare, simulated learning experiences are a valuable tool that helps learners practice required skills in a safe, low-stakes environment. These simulated learning experiences are key when it comes to training healthcare professionals to do everything from fundamental skills to complicated procedures. However, it is typically the case that providing simulated learning experiences for the purpose of training healthcare professionals can be a laborious and costly endeavor. Moreover, these initial hurdles of cost and manpower are often major limiting factors for smaller healthcare organizations that do not have the necessary resources to offer such training experiences. This poster aims to highlight how an AI tool called Mursion can help make simulated learning experiences more accessible to smaller healthcare organizations. Mursion is a software that combines AI tools with live human performance to create simulated learning experiences that are entirely virtual. The poster showcases a virtual simulation series created by Bowling Green State University’s Virtual Simulation Program for an organization known as Mental Health America of Northern Kentucky and Southwest Ohio that focus on training Mental Health First Aiders how to apply specific mental health first aid techniques when conversing with someone experiencing a mental health crisis.

Presentation Title: Exploring the Intersectionality of Poverty and The Child Welfare System: A Focus on African American Families (No. 3)

Authors: Audrey Simon, Social Work and Sociology major & Courtney Miller, Social Work major

Abstract: African American children are investigated and put in foster care at higher rates than white children, usually because of poverty rather than abuse or neglect. This shows a systemic problem within the child welfare system that can cause emotional harm and fails to address the main issues. The purpose of this study is to understand the impact of poverty on African American families within the child welfare system and find what can be done to reduce racial disparities. We reviewed nine articles, showing that most cases involve neglect and that African American families are less likely to be offered support services. The main limitations were ethical concerns and a lack of existing research. The implications include improving data collection, expanding community programs, ensuring families know their rights, addressing systemic racism and bias, and increasing funding to better support low-income families.

Presentation Title: Interpretable Cardiac Dynamics: Hidden Markov Model Co-Clustering for Echocardiogram Video Analysis (No. 14)

Authors: Shakhnoza Takhirova* (CS), Shuchismista Sarkar (ASOR)

Abstract: Deep learning models are increasingly used to analyze medical images and videos, but their "black box" nature makes it difficult to understand how they reach conclusions—a critical limitation in healthcare. We propose a Hidden Markov Model (HMM) based co-clustering approach for analyzing echocardiogram videos as a contribution toward developing explainable AI tools. Our method simultaneously identifies hidden temporal patterns (cardiac states) and groups similar image regions, capturing both how the heart changes over time and which areas exhibit similar behavior. We applied this approach to the EchoNet-Dynamic dataset, using systematic model selection to identify the optimal configuration. Our model discovered three biologically meaningful cardiac states and 16 spatial clusters. It demonstrated intelligent adaptive behavior, successfully detected precise volume changes in ventricles and atria, focused on clinically relevant structures like atrial valves, and automatically excluded uninformative regions. This work demonstrates that HMM co-clustering can serve as an contribution to developing explainable AI tools for medical imaging. The framework's transparent identification of clinically relevant temporal states and spatial patterns offers an interpretable alternative that helps clinicians understand which image regions and cardiac phases drive diagnostic decisions, directly supporting trustworthy AI for the public good.

Presentation Title: The use of an AI text-to-video generator to help undergraduate students explore public health history (No. 12)

Authors: Philip John Welch, PhD, MHSA Graduate Coordinator, Department of Public and Allied Health

Abstract: Regardless of major, college graduates increasingly need strong AI skills, such as AI prompting, to be successful in their chosen fields. According to Florida State University, AI prompting is “the process of interacting with an AI system by providing instructions to achieve a desired outcome”. To hone their prompting skills, 35 students in Introduction to Public Health (Fall 2025 semester) used a free AI text-to-video generator to create 1-minute educational videos about historically important public health events of their choosing. Students located two peer-reviewed journal articles about their event to help write their narrative prompts (100–200 words) using clear, engaging, and logical script structure. Final prompts and videos were uploaded to Canvas and presented in class. Improvements in prompt writing were needed to generate appropriate scenery, mood, background music, and voice narration. However, students enjoyed the assignment and learned how to write better AI prompts while also having fun. The course instructor spliced the videos together using Microsoft Clipchamp. For this presentation, the full 30-minute video with closed captioning will be displayed on a 27” computer monitor along with the prompts for attendees to watch. Full assignment details and the grading rubric will also be presented.

Presentation Title: “I’m so tired of waiting”: Imagining A more Just Non-Emergency Medical Transportation System (No. 8)

Authors: Justin Rex* Political Science, Stephanie Villella* Center for Regional Development, Maddi Menich* Center for Regional Development, Maggie Fuller* Center for Regional Development

Abstract: Transportation access is an important social determinant of health. Low-income women and mothers face significant transportation barriers due to a “pink transportation tax” from shouldering caregiving and medical appointment responsibilities, often on public transportation infrastructure that does not accommodate strollers, groceries, or wheelchairs. Non-emergency medical transportation (NEMT) systems are one potential solution, providing Medicaid recipients door-to-door ride services to and from appointments and pharmacies. Our study evaluates how well NEMT addresses these barriers. Our data is drawn from semi-structured interviews with 36 pregnant birthing individuals or those who recently had a child and live in Toledo, Ohio. The interviewees are part of a larger transportation study that provided participants monthly cash or rideshare vouchers for 6 months in exchange for monthly interviews and one post-study interview, for a total of 252 interviews. We use thematic content analysis and NVIVO to code themes from NEMT-specific questions and general transportation check-in questions in which NEMT experiences arose organically. Results find few positive experiences but frequent neutral or negative ones, along with ways riders adapted the program to their needs. The results also indicate ways to improve service delivery and possibilities for reimagining what is considered a medical/health-related destination.