Stander Symposium: All projects - 2025

Follow

• 

  Reflections on the Communication Master's Degree

  Pat Enright, Dori Miller, Gift Olalusi, Emily Parker, Kaelene Walter

  The Communication Graduate Practicum students created portfolios reflecting on their graduate studies. They collected assigned readings, projects, papers, and other elements from their graduate experience to craft a portfolio telling their masters degree story. Today they share highlights from their stories.

• 

  Rehabilitation programs and their effectiveness on reducing recidivism and arrest rates among juveniles

  Matthew Pochatek

  For this research project, I aim to explore various rehabilitation programs, with a focus on substance abuse treatment and mental health counseling, to assess their impact on juvenile recidivism. The primary research question I seek to answer is: Do rehabilitation programs reduce recidivism and arrest rates among juveniles, and if so, to what extent? The key outcome I intend to measure is the proportion of juveniles who, after participating in specific rehabilitation programs, either offend or re-offend. I will gather evidence of effectiveness for each rehab program and compare them. The independent variable in this study is the type of facility treatment received by individuals, such as mental health counseling or drug and alcohol rehabilitation. The dependent, or responding variable, is the recidivism rate among juveniles who have undergone treatment. Recidivism is defined as "a tendency to relapse into a previous condition or mode of behavior," and within criminology, it specifically refers to "the recurrence of criminal behavior, particularly following punishment"

• 

  Religion and mental health around the world: Similarities and differences

  Aiden Grumbos, Jack Ishmael, Kenna Ryan

  This is a literature review exploring how religion influences people's mental health and the similarities and differences seen in the ways different religions address mental health issues.

• 

  Religious Rhetoric in Our Lives

  Audrey Alloto, Kolby Andwan, Sean Banks, Jordyn Boron, Nicole Chelew, Jack Doherty, Thomas Dwyer, Charles Frech, Thomas Fus, Dyan Garner, Gia Gentile, Caitlin Grevey, Elisabeth Gromofsky, Eileen Harris, Brady Hopkins, John Howland, Rylie Kollin, Maja Kosir, Emma Kovacevich, John Landis, Lotus Lazzara, Elena Loughlin, Collin Maclang, Maria Manzella, Ryan Marderosian, Ashley Mayr, Rachel Morgan, Owen Mullen, Gracelin O'Malley, Taylor Powell, Natalie Rach, Laney Reiss, Colin Richardson, Caitlyn Russell, Patrick Schwartz, Grace Scott, Jaden Secker, Natalia Serpe, Olivia Shirk, Kaitlyn Stehlik, Mary Stephens, Samantha Thomas, Thomas Thompson, Erica Velecela, Joseph Wallace, Alyson Walter, Elisabeth Watson, Hannah Witman, Jacob Wozniak, Megan Zielsdorf, Charlie Zieziula

  The students in CMM 357 Religious Rhetoric spent the semester analyzing their own examples of rhetoric that explicitly or implicitly were religious in nature. Each group is sharing one of their reports that may focus on definitions of rhetoric, semiotics, dramatism, power, and/or civil religion.

• 

  Research Paper Quality Recognition

  Sadwik Gummadavelli

  Knowledge and innovations are shaped by using the quality and credibility of the scientific research. There always remains a challenge how to distinguish between impactful high-quality research and flawed. This project proposes a very systematic approach to classifying the research papers into good and bad categories where bad papers are those retracted from journals or conference proceedings and good papers are characterized by high citation counts. We explore the underlying factors that contribute to a papers scholarly influence or its eventual rejection by analyzing by citation patterns publication meta data and retraction records. We used machine learning models and feature extraction techniques to identify anomalies, trends and potential predictors of research quality. The findings of our study insights into highlighting the importance of citation behavior, the dynamics of academic publishing and scientific accountability. This study adds to the larger conversation about academic impact evaluation and lays the groundwork for automated tools that can help assess the reliability of research papers.

• 

  Resolving the function and evolution for two transcription factor genes that pattern a sexually dimorphic fruit fly pigmentation trait

  Logan A. Brubaker, Hayley Long, Allison Pavlus

  Animal morphological traits develop through the actions of and evolve through changes in genomic-encoded Gene Regulatory Networks or GRNs. Therefore, a robust understanding of the evolutionary developmental biology of traits requires knowledge about the architecture of GRNs and how such architecture evolves. Generally speaking, GRNs are hierarchical in structure, with their highest tier consisting of regulatory genes like transcription factors and the lowest tier consisting of the realizator genes whose encoded proteins end up making the trait. For several evo-devo model traits, some of the regulatory and realizator tier genes are known and their evolution have been chronicled. However, the architecture and evolution of an entire GRN has remained out of reach. This includes the GRN responsible for the sexually dimorphic pigmentation on the abdomens of Drosophila fruit flies. Here, new insights will be shared for two upper-tier transcription factors in the Drosophila melanogaster abdomen pigmentation GRN whose function and evolution have remained mostly un-studied.

• 

  Response of Bats to Solar Energy Developments

  Connor Kurz

  Solar energy developments are highly beneficial to renewable energy production, yet can fragment wildlife habitat and can affect sensitive mammal species, such as bats. Information on how bats are affected by renewable energy developments is well known for wind energy but very limited for solar energy. Ohio is home to 11 different bat species all with different traits, and all of them state protected. To better understand the response of bats to solar energy developments, we used acoustic monitoring techniques to record bat activity during the summer and fall at solar (n=5), forested (n=3), and prairie (n=3) sites. Bat activity was quantified using the number of passes per night and we used generalized linear mixed models to examine the relationship between activity and different environmental and site covariates. Using the same approach, we also examined species richness and diversity. Overall, we predict that solar energy developments will have a negative effect on bat activity and species composition compared to natural sites due to the reduced habitat complexity and therefore insect diversity. Our study will provide valuable insights on bat ecology in relation to sustainable energy and help inform on how to best mitigate and manage any impacts of these developments on bats in western Ohio.

• 

  Role of Hedgehog in eye-head specification of Drosophila Melanogaster

  Santosh Kumar

  Hedgehog (Hh) signaling is a conserved pathway in both vertebrates and invertebrates, playing a crucial role in growth and differentiation. In the eye imaginal disc of Drosophila, Hh is expressed posterior to the developing morphogenetic furrow (MF) and in the dorsal anterior region within the developing ocellar field. While the role of Hh in MF progression is well understood, its function in the dorsal anterior region remains unclear. To investigate this, we overexpressed Hh in the Defective proventriculus (Dve) domain and interestingly observed enlargement of the ventral eye field. This finding suggests a potential interaction between Hh signaling and region-specific dorsal and ventral pathways, which may shed light on the molecular mechanisms governing eye versus head fate determination in Drosophila. Furthermore, we found that Hh signaling sustains ventral eye field expansion and photoreceptor differentiation by inducing decapentaplegic (dpp) expression in the ventral region through the transcription factor Cubitus interruptus (Cirep). Based on our preliminary data, we propose that increased dpp levels may elevate teashirt (tsh) expression. While tsh is expressed in both dorsal and ventral regions, it specifically promotes ventral eye development by inhibiting the homothorax (hth)-wingless (wg) positive feedback loop. This regulatory interaction may be restricted to the ventral region due to differences in molecular context, as the dorsal region may rely on Pannier (pnr) and Dve to sustain wg expression. To test this hypothesis, we will use immunohistochemistry and qRTPCR based approach to determine the level of diferent dorsal and ventral factors involved in dorsal and ventral eye-head specification in the context of gain of function and loss of function of Hh and Dpp.We strongly anticipate that this study will enhance our understanding of the molecular interplay governing dorsal and ventral eye versus head specification in Drosophila melanogaster. Keywords: Hedgehog, Dpp, Eye specification, Drosophila

• 

  Roman Aesthetics as Symbols of Power

  Camden Daley

  Since the fall of Rome, political movements have drawn upon its aesthetics to project power and instill pride in their base. This research explores why leaders and governments emulate Roman symbols and architecture, arguing that these choices serve as a deliberate strategy to inspire confidence and establish authority. By invoking Rome, political entities seek to connect themselves to a legacy of strength, stability, and imperial grandeur. This study examines three case studies: the United States, Napoleonic France, and Fascist Italy. These three examples will serve to analyze how each adapted Roman imagery to suit its political ambitions. The United States, in its founding, adopted neoclassical architecture and the Roman eagle. This is shown in Kumar’s (2017) “The Idea of Empire,” in which he describes America as being akin to the grandeur of Rome. Napoleon Bonaparte used Roman triumphal arches and imperial symbols to emulate the aesthetics associated with Roman emperors and conquerors. Mussolini, in his quest to restore Italian greatness, explicitly tied his regime to Rome, employing the use of the fasces and monumental architecture to evoke imperial dominance. This is most apparent through the Italian EUR complex, which combined modern and Roman architectural styles in order to emulate the appearance of Roman spaces, as explored in Muller’s (2012) “The imperial mirror: Rome as reference for empire.” This research demonstrates how aesthetics serve as more than decoration, they function as powerful political tools, shaping public perception and reinforcing authority. The study contributes to broader discussions on the role of historical identity in political messaging, emphasizing how the past continues to shape modern expressions of power.

• 

  Roommate Selection Methods & Satisfaction: Does Choice Make A Difference?

  Colleen G Hogan, Ava Jacomet, Maria Shomo, Mary Stephens

  This study examines the relationship between the roommate selection method and student satisfaction with their housing experience at the University of Dayton. Specifically, it investigates whether students who choose their roommates through Dayton Matching, social media, or other means report higher levels of satisfaction with their living conditions. By analyzing these factors, this research aims to provide insight into how different selection methods influence students’ housing experiences and whether having control over the choice of a roommate leads to greater satisfaction.

• 

  Saturn Sports Business Pitch

  Henry Veeneman

  This presentation features a startup business pitch by senior Hank Veeneman, founder and CEO of Saturn Sports. Founded by University of Dayton students, Saturn Sports is dedicated to advancing sports safety through innovative technology. Their current focus is enhancing helmet security in football, recognizing that a properly tightened chinstrap is crucial for player protection. Many athletes fail to fully secure their chinstraps, increasing the risks of injury. Saturn Sports is developing a cutting-edge solution to ensure proper chinstrap tightening, improving safety and reducing injuries at all levels of the game.

• 

  Sea Ice Data Generation Using Diffusion Models

  Aqsa Sultana

  The increasing frequency of extreme weather events due to global warming poses significant threats to ecosystems and human life. One of the primary indicators of climate change in the Arctic is the formation of melt ponds on sea ice. However, the lack of large-scale, annotated Arctic sea ice datasets presents a major challenge in training deep learning models for predicting the dynamics of these melt ponds. In this study, we propose the use of diffusion models, a class of generative models, to synthesize Arctic sea ice data for the analysis of melt pond formation.Diffusion models generate realistic new data by learning the distribution of existing data and iteratively transforming a simple distribution into a more complex one through a noise-adding process. During training, noise (such as Gaussian noise) is added to the data, and the model learns how to reverse this process. After training, the model can generate new, realistic data by starting from random noise and gradually transforming it to match the distribution of the original data. During inference, the model uses conditioning information alongside the noise input to guide the generation of samples that adhere to specified conditions.For training the model, we used high-resolution aerial imagery from the Arctic region, collected during the Healy-Oden Trans Arctic Expedition (HOTRAX) in 2005, and NASA’s Operation IceBridge DMS L1B Geolocated and Orthorectified data from 2016. To evaluate the quality of the synthetic images, we employ the Chromatic Similarity Index (CSI), a metric for assessing chromatic similarity between the original and generated images. This approach demonstrates the potential of diffusion models for generating synthetic Arctic sea ice data to further understand melt pond dynamics.

• 

  Seamless AR Safety Training

  Abhijeet Gupta

  The construction industry remains one of the most hazardous sectors, with a worker losing their life every 99 minutes due to work-related incidents. Despite ongoing advancements in safety protocols, training programs, and regulations, the industry continues to struggle with preventable hazards, particularly falls, electrocutions, and struck-by incidents. Traditional safety measures, including PPE, signage, and worker training, often fall short due to complacency, distractions, and limited real-time hazard awareness. To address these challenges, technology-driven safety solutions are becoming increasingly essential. This study aims to develop an advanced construction safety framework that leverages Augmented Reality (AR) simulations to enhance hazard recognition and safety training. By creating realistic, interactive hazard scenarios, workers can experience high-risk situations in a controlled, immersive environment, leading to improved hazard awareness and response. The AR simulations will focus on four primary hazards—falling, tripping, electrocution, and falling objects—using computer vision and AI-driven hazard detection for real-time safety training. In addition to developing the AR-based framework, this research will systematically review and evaluate twelve emerging safety technologies to assess their effectiveness in preventing construction hazards. These include – Building Information Modeling (BIM), Augmented Reality (AR) & Virtual Reality (VR), Drones, Computer Vision, Wearable Sensors, Internet of Things (IoT) Devices, Predictive Analytics, Proximity Alert Systems, AI/Machine Learning (AI/ML), Safety Management Systems, Digital Twins, Robotics & Automation. The study will construct a technology taxonomy to categorize these solutions and conduct a comparative analysis of pre-2020 and post-2020 research, highlighting how recent advancements have addressed prior limitations. By integrating real-time hazard detection using AR with a comprehensive review of emerging safety technologies, this research aims to provide insights for improving construction safety standards, reducing major workplace fatalities, and enhancing worker training and preparedness.

• 

  Sector Return Cyclicality: Investigating the Relationship Between Past and Future Performance

  Christopher Casey, Dominic Fiorilli, Kylie Jackson, Michael Kondritz, Jordan Nelson, Cannon Spelman

  For this project, we are analyzing whether sector returns and outperformance relative to the S&P 500 have a predictive relationship with future performance. Using historical sector data dating back to January 1, 1990, we will run regressions across multiple time horizons ranging from 1 to 10 years to test for cyclicality and determine whether past outperforming sectors are more likely to continue outperforming or revert to underperformance. By identifying potential patterns in sector returns over different time frames, this analysis aims to assess whether cyclical trends exist and if they could be used to inform investment decisions. Future steps include evaluating the strength and consistency of these relationships and exploring additional statistical techniques, such as lagged regressions, to refine the findings.

• 

  Self-Supervised Contrastive Learning for BCI system

  Abdulbasit Alhinqari

  As the demand for various AI applications continues to grow in importance for futuristic aspects of life, non-invasive Brain-Computer Interfaces (BCIs) are expected to become one of the top priorities. BCIs enable humans to control surrounding equipment and devices through a direct communication link from the brain. These systems often rely on the classification of Electroencephalogram (EEG) signals, which are recordings of human brain activity. Given this potential, an increasing number of researchers and scientists are focusing on this field.Traditionally, various algorithms have relied on manual feature extraction to classify EEG datasets. However, recent advancements in Convolutional Neural Networks (CNNs) and deep learning architectures have demonstrated significant success in tasks such as computer vision, natural language processing, and contextual analysis, largely due to their ability to perform automatic feature extraction. Despite their success in other domains, these methods still struggle to generalize effectively on EEG signals due to their non-stationary and random nature.This work focuses on EEG-based BCI systems that leverage CNNs and deep learning tools. Specifically, it explores the application of self-supervised contrastive learning techniques for the classification of motor imagery (MI) actions.

• 

  Senegal as a Case Study: Africa’s Economic Shifts in a Globalized World

  Julie Pugh

  This presentation examines the economic transformation of Senegal, focusing specifically on how the state is navigating its evolving relationships with foreign influences such as France, China, and international institutions. As a former French colony, Senegal continues to confront structural economic dependencies that tie its success to the West, particularly through its currency and the presence of French multinational corporations. At the same time, rising world powers, such as China, have become increasingly important in the region as they aim to exert influence previously held by colonial powers through infrastructure deals and trade partnerships that impact Senegal’s economic policies, investments, and labor markets –raising new questions about economic sovereignty. These external pressures loom large as Senegal pursues economic diversification, labor market reforms, and regional integration in response to demographic changes and in an attempt to assert greater control over its economic future. Previous research on postcolonial African economies has examined structural dependencies and foreign investment patterns, but this presentation seeks both to provide a contemporary perspective on how Senegal is responding to shifts in global power and looking to promote a sustainable economic future. In doing so, this presentation draws on a range of sources, including economic and policy reports from international organizations, scholarly analyses of African economic development, and recent news and think tank publications. Ultimately, this presentation seeks to utilize Senegal as a valuable lens through which broader conclusions on Africa’s economic modernization and position in a shifting global order can be drawn.

• 

  Shielded RL: A Safe Reinforcement Learning Concept

  Shruti Singh

  Artificial Intelligence (AI) continues to be a part of our everyday life in ways one thoughtimpossible. From instantly curated music playlists and virtual experiences to autonomousvehicles on the roads, everyday life has become intertwined with AI’s remarkablecapabilities. A significant driving force behind these advancements is reinforcement learning(RL), a field of AI that excels at conquering challenging tasks through interactions withdynamic environments. Its adaptability has inspired solutions—from steering driverless carsand maneuvering robot fleets to managing warehouse logistics and guiding patienttreatments—reinforcement learning effortlessly demonstrates its power across diversedomains. Unfortunately, similar to any popular field, RL-based systems also draw theattention of malicious actors who look for cracks in these sophisticated models. Inhigh-stakes situations, malicious noise injected at precisely the right time can compromisethe integrity of an RL system, leading to potentially disastrous outcomes. Think of aself-driving car receiving deceptive signals about obstacles or lane markings—turning whatshould be a smooth journey into a hazard for passengers and pedestrians alike. This kind ofstrategic tampering, known as an adversarial attack, can derail an RL agent if we fail to usetailored defense mechanisms. Although there is no universal, foolproof remedy foradversarial onslaughts, developing strategies that detect, evaluate, and counter suchthreats—based on the specific needs and vulnerabilities of a given system—forms thebedrock of robust protection. This brings us to the idea of shielded reinforcement learning(shielded RL), which acts like a security gate that stands between the learning agent andpotential disaster. Much like the way we described adversarial attacks in everyday terms,shielded RL can be understood as a “safety net” for the agent: it continually monitorspossible actions and prevents reckless or unsafe choices before they become catastrophic.In other words, while the RL agent learns by trial and error, the shield remainsever-vigilant—subtly guiding it around pitfalls and ensuring that it does not stray intodangerous territory. By embracing shielded RL, we can build robust reinforcement learningsystems that not only perform impressively, but also stay safe against determined attackers.

• 

  SHIFT: Portfolio Show at Stander Symposium

  Avery Bocock, Kevin Brun, Noah Davisson, Sebastian De Leon Osorio, Mary Dent, Elaina Doggett, Erin Doherty, Isa Evans, Jillian Fahey, Kevin Figueroa, Laurel Grelle, Maggie Grunden, Quinn Heisey, Emily Kintz, Katherine Lawlor, Elaina Lear, John Maloney, Ava Mendenhall, Lucy Miles, Jordan Mitchell, Maddison Mitchell, Catherine Orban, Jacob Owens, Jonathan Quiroz, Hannah Schultz, Madelyn Selong, Isabella Winkler

  This Capstone event includes all 25 seniors graduating in 2025 with a degree in Graphic Design presenting the culmination of their coursework within and beyond the Department of Art and Design. Students will share their portfolios—research, creative process, and outcomes—in the context of a formal presentation of both classroom and real-world projects. Work will be shared simultaneously.

• 

  Silent Struggles: Stigma, Mental Health Disclosure, and Social Support in Older Adulthood

  Dori Miller

  As the global population ages, mental health concerns among older adults have become an increasingly important public health issue. Despite growing awareness of mental health stigma, little research has explored how older adults navigate stigma, selective disclosure, and social support in the context of mental health. Stigma, including both public and internalized stigma, can significantly impact older adults' willingness to seek mental health support, often leading to selective disclosure—the decision to reveal or conceal mental health struggles based on concerns about social acceptance. This study applies Stigma Management Communication (SMC) theory (Meisenbach, 2010) and the conceptual framework of social support (Taylor, 2011) to examine how older adults manage disclosure and the role of social networks in shaping these decisions.Using qualitative methods, this research employs semi-structured interviews with adults aged 65 and older to explore their lived experiences with mental health stigma, disclosure strategies, and social support. This approach provides an in-depth understanding of how stigma influences help-seeking behaviors and interpersonal relationships. Findings from this study will contribute to a more nuanced understanding of how older adults negotiate mental health disclosure, maintain social connections, and mitigate stigma’s negative effects.Addressing this gap in the literature is critical for developing stigma-reduction strategies and ensuring that mental health interventions are inclusive and effective for older adults. Without addressing these challenges, older adults may continue to experience barriers to care, leading to increased isolation and unmet mental health needs. By shedding light on these issues, this study aims to inform policies, healthcare practices, and social support initiatives that foster a more supportive environment for aging individuals. Ultimately, this research seeks to promote mental health equity, social inclusion, and improved well-being for older adults navigating mental health challenges in later life.

• 

  Single-ended in-situ atmospheric turbulence strength characterization using deep neural networks.

  Prabjeet Saggu

  In Free Space Optical (FSO) communication systems, precise characterization of atmospheric turbulence strength is essential for propagation systems. This study investigates the use of Deep Neural Networks (DNNs) to evaluate atmospheric turbulence strength by analyzing scintillation patterns observed in double-pass laser beam propagation scenarios. Objective of this project to develop a DNN-based sensing data processing model capable of predicting the strength of atmospheric turbulence (��_��^2​) from simulated scintillation patterns in two distinct scenarios: single pass propagation and double pass propagation systems.

• 

  Single Year Sector Return Compared to S&P 500 since 1990

  Michael Jablonski, Chasen Kern

  For our project, we calculated the single year return for each of the sectors of the S&P 500 (excluding real estate) since January 1, 1990. After calculating the single year return, we created a table to visualize returns compared to the benchmark. In this table, we can observe how industries returned nominally when directly related to the S&P 500. This table reaffirms our findings in the 5-year period as each sector can have multiple years of either over or underperforming the index. This allows us to conclude that performance in a single year does not necessarily drive a mean reverting tendency in a one-year period but may possess a mean reverting tendency over a longer time frame. The next step in confirming this would be creating a regression that lags the performance of each sector. This can help us determine with statistical confidence over various time periods. In the short-term momentum appears to be a determinate of results, but potentially shifting as the time gets stretched longer.

• 

  Skechers Closing Stock Price Forecasting Using Time Series Analysis

  Sydney Dobyns

  This study analyzes the historical closing stock price of Skechers to develop a predictive model for future price movements. The dataset spans from the early 2000s to December 31, 2019, and we will forecast stock closing price for the period 2020-2023. Analyzing the dataset reveals that the original time series is non-stationary which requires transformation before applying forecasting models. Various time series models are evaluated based on performance metrics such as Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), and Mean Absolute Percentage Error (MAPE). By comparing these models we aim to determine the most accurate approach for predicting Skechers' future stock prices. Given the ever-changing nature of the retail industry, where consumer trends, economic conditions, and competition continuously fluctuates. It is important to develop a reliable forecasting method. Accurate predictions can assist investors, business leaders, and analysts in making informed decisions, allowing them to better navigate market uncertainties and strategize for future growth.

• 

  Social Emotional Learning in Elementary School

  Molly O'Riordan

  Implementing social-emotional learning (SEL) in elementary schools fosters students' emotional well-being, improves behavior, and enhances academic performance. This literature review focuses on the importance of starting SEL programs at a young age. As well as how SEL programs help children develop critical skills such as self-regulation, empathy, and interpersonal communication, leading to a positive classroom environment and better overall student outcomes both academically and socially.

• 

  Social Media and Its Effect on Political Engagement

  Brianna Comstock, Jessica Snyder

  This research investigates how social media effects political engagement, polarization, and discourse. It is a secondary study that utilizes data from The Civic Network: A Comparative Study of the Use of Social Media for Enhancing Young People's Political Engagement (Australia, United Kingdom, and United States, 2013).

• 

  Social Media Engagement with Traumatic Brain Injury Prevention, Recovery, and Community Re-Entry Resources

  Emma Braden

  Traumatic Brain Injuries (TBIs) can result in cognitive, physical, and social-emotional effects that last a lifetime. Inefficient resources can be a significant barrier to care. Internet use, particularly social media, has become an effective way to communicate essential and educational information. Social media can foster connection among those affected by TBIs. Researchers evaluated engagement via analytics on three social media sites on three different platforms that shared information on TBI prevention, recovery, and community re-entry resources. The results of an ANOVA indicated significantly higher engagement when posts were boosted (p = <.001). There was also higher engagement across Facebook and Instagram when compared to Twitter/X. No significant differences were found among engagement across content types; therefore, TBI content creators should continue to post resources relating to prevention, post-injury, statistics, and general information. This study indicated that Facebook and Instagram may be the best social media sites suited to disperse boosted traumatic brain injury resources and content, bridging the gap between healthcare and community.