Sakowich Center for Undergraduate Research Current Projects

Most jobs for working-class adults include physically inactive tasks and exposure to unhealthy food choices. Workplace wellness programs (WWP) provide unique opportunities to target cardiovascular disease risk and other contributors to serious health conditions in large employee populations by capitalizing on existing resources. Despite the benefits of WWP for physical and mental health, engagement and adherence remain a challenge. The proposed mixed-methods pilot study will recruit Merrimack Faculty and Staff and assess preferences for the content and structure for a WWP with greater engagement and adherence potential. The specific objectives of this proposed mixed methods pilot study are to address the: 1) rates of willingness to participate in a WWP, 2) preferences for the content and structure of a WWP, and 3) perceived barriers and facilitators to engagement and adherence, prior to the development of the first iteration of the WWP. Participant responses will be recorded using a combination of surveys and semi-structured interviews. Results from this study will provide direction and guidance towards the development of the first iteration of the Warrior Health Improvement Program (WHIP).

Youth Voice is hosted at the Merrimack Valley YMCA and runs for 3 weeks. This program focuses on helping middle school students understand their identity and how to use their unique skills and talents to make a positive difference in the local community. The SCURCA students work all summer co-designing the program, and then they facilitate the program in July. The summer program teaches the SCURCA students about building community partnerships as a component of engaged research. This year’s students helped foster and build community partnerships with the Merrimack Valley YMCA, Ateno Dominicano, Groundwork Lawrence, the Museum of African American History in Boston, and mental health practitioner Stephanie Letourneau. Students from Merrimack’s Early College program are also incorporated into the program as co-leaders, who are mentored by the SCURCA students. They conduct research on other projects including studies that range from English language learning initiatives, studies on empathy, global civic engagement, and the impact of COVID-19 on the political socialization of young people. 

Given that both sleep disruption and mild traumatic brain injury (mTBI, or concussion) can impact emotion regulation, it is possible that improving sleep quality may reduce the emotion dysregulation associated with mTBI. Therefore, sleep extension may be an effective way to improve some of the symptoms of mTBIs. As the prevalence of mTBI is extremely high, it is critical to determine ways to mitigate impairments. Kurdziel and Seitchik aim to assess the possible therapeutic function of a daytime nap to alleviate some emotion regulation symptoms, which could, in turn, reduce anxiety, depression, or other mental health outcomes commonly associated with mTBI.

Data-driven Machine Learning (ML) has emerged as a promising approach to building predictive models from medical data, which is collected in huge volumes by modern healthcare systems. Unfortunately, existing medical data is not fully utilized by the industry since there are data silos and privacy concerns with accessibility to the data. Health data are highly sensitive, subject to regulation and cannot be used for research without appropriate patient consent and ethical approval. In addition, these datasets have a significant business value and companies are not willing to share with researchers for the fear of losing a considerable amount of revenue. The main focus of this project is to explore a new technology called Federated Machine Learning (FML), which is a learning method that seeks to address the problem of data sharing, data governance and privacy by training algorithms collaboratively without exchanging the underlying datasets.

The computer and data sciences department is excited to develop a summer research experience opportunity for motivated undergraduates in our programs. This project will expose students to research methods traditionally found in computational sciences, specifically the new and transformative area of federated machine learning (FML), which is staged to have unprecedented impact in the healthcare community.

Models used to predict student achievement in STEM programs focus heavily on narrow paradigms of support (e.g., academic support) to predict outcomes directly related to student course of study (e.g., academic performance in introductory STEM courses). The current work instead focuses on non-cognitive factors (e.g., constructs of self-efficacy, self-perception, school- engagement and emotional-distress) to examine how the pathways between such constructs impact a larger understanding of academic achievement (e.g., non-major GPA, major GPA, retention). Validating the theoretical pathways between constructs of social-emotional support and academic outcomes in early college STEM students will provide evidence for interventions. Undergraduates today have higher rates of identified mental health issues than previous generations of students. These issues are exacerbated in fields such as STEM areas, where students are less likely to seek help and more likely to see a stigma attached to their issues (Lipson et al., 2016). The current work, therefore, instead focuses on non-cognitive factors (e.g., constructs of self-efficacy, self-perception, school-engagement and emotional-distress) to examine how the pathways between such constructs impact a larger understanding of academic achievement (e.g., non-major GPA, major GPA, retention).