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Harrisburg, PA – Welcome to your May 2024 Harrisburg University of Science and Technology (HU) Research Roundup! Our talented faculty members are busy presenting at conferences, publishing boundary-pushing research, and building fruitful partnerships with other schools, government agencies, and organizations.

It’s been a great month for research and recognition at HU, so let’s get right to it!

1. Tonya Miller and Chris Condran Published in Physical Therapy & Rehabilitation Journal

Tonya Miller, PT, DPT, PhD and Chris Condran, PT, DPT, EdD, DAC, MBA, MS have published a study in Physical Therapy & Rehabilitation Journal called LGBTQIA+ Cultural Competence in Physical Therapist Education and Practice: A Qualitative Study from the Patients’ Perspective.

According to the authors, “The purpose of this study was to explore the experiences of cultural competence and humility among patients of the lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA+) community in physical therapy. [We] sought to understand the perspectives of adults who have received physical therapy and identify as a member of the LGBTQIA+ community.”

The results show that an LGBTQIA+ patient’s experience is influenced by the provider’s cultural acceptance and the resulting power dynamics that impact LGBTQIA+ patients’ comfort, trust, and perceptions of care. Enhanced patient experiences were observed more frequently with providers that possessed elevated levels of education or experience with this community.

Regarding the long-term impact of the study, the authors state, “Awareness of the underlying issues presented in these themes will assist in the development of effective solutions to improve LGBTQIA+ cultural competence among physical therapists and physical therapist assistants on a systemic level.”

2. Sonam Gupta and Kayden Jordan Publish Paper in Health Communications

HU Data Science PhD student, Sonam Gupta, along with her co-author Kayden Jordan, PhD, Assistant Professor of Social Analytics, had their paper, Does the Gender of Doctors Change a Patient’s Perception?, accepted for publication in Health Communications.

In the past, researchers have utilized various data sources, such as social media, admission applications, and letters of recommendation, to identify gender-based differences in linguistic data. A new area of interest is online physician review websites. These platforms have become popular resources for patients as they choose their physicians. In this study, the researchers employed two natural language processing (NLP) techniques to analyze texts and identify gender-based linguistic differences from patients’ perspectives.

“How we write language matters a lot, especially on platforms where we are writing reviews for physicians,” said Sonam Gupta. “The implicit gender biases are hard to identify, which is why there is a strong need to make people aware of such biases, especially in healthcare interactions, for better patient outcomes.”

The findings indicate that female physicians receive more reviews highlighting their bedside manner, personable skills, and warmth. Reviews suggest that patients are more satisfied with female physicians, perceiving them to have more positive traits than their male counterparts. The study underscored the importance of awareness regarding potential gender stereotypes and perceptions for both patients and doctors.

3. HU Attends 28th Annual Consortium for Computing Sciences in Colleges Northeastern (CCSCNE)

Sa Liu, PhD (Assistant Professor of Interactive Media), Philip Grim (Computer Science instructor), and two students, Vaishnavi Padala (Human-Centered Interaction Design), and Arianna Hall (Interactive Media) represented Harrisburg University at the 28th Consortium for Computing Sciences in Colleges Northeastern (CCSCNE) annual conference at Saint Rose College in Albany, NY.

The team presented their Presidential Research Grant research project: Assessing Risks, Challenges and Opportunities of Generative AI in Computer Programming Education. The presentation was well received. Other team members include HU’s Dr. Brian GreyChad ChuDr. Thomas McManus, student research assistant Nathan Meyers, and Dr. Ryan Watkins from George Washington University.

“Education is something I find myself fascinated with, and the ability to explore how the latest technology is shaping it is profound,” said Arianna Hall. “Going to the conference helped me better grasp a field I work closely with, yet often feel disconnected from, as someone who primarily works on UX [user experience] research. It was insightful to learn how coding is impacted by AI, how it can change learning outcomes, and how I can shape my future to further my education with grace.”

Vaishnavi Padala added, “The conference provided valuable insights into themes I’m passionate about: making CS [computer science] education accessible, integrating empathy into CS education, AI and machine learning in education, and innovative teaching methods for introductory, interdisciplinary, and liberal arts computing. I appreciated the environment’s openness, making it welcoming for newcomers to explore the research landscape and find like-minded collaborators.”

4. HU Research Team Publishes Research Paper with Biomark Diagnostics

A team comprised of researchers from Biomark Diagnostics Inc. and Harrisburg University alumni, Maria Vaida, PhD (2020 HU alumnus), and Bharadwaj Popuri (2021 HU MS alumnus), has had their paper accepted for publication in the International Journal of Molecular Sciences.

The paper, entitled Identifying Robust Biomarker Panels for Breast Cancer Screening, provides a deep dive into using metabolomic profiling for the advanced detection of breast cancer. Metabolomics studies the substances within organisms responsible for breaking down food, medications, and body tissue.

The team believes they have identified biomarkers that could greatly improve screening efficiency and save patient lives. They used multiple machine-learning models, including ensemble learning, logistic regression, multidimensional scaling, and support vector machines, for classification. The feature set with the highest performance achieved 98 percent accuracy, demonstrating the potential for a cost-effective, non-invasive breast cancer diagnostic and screening tool. In the US, health experts predict more than 310,000 newly diagnosed cases of breast cancer, making this research highly timely.


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