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Governors State University Board of Trustees Committee of the Whole, Video Recording June 17th, 2024 Pt. 2
No full textGovernors State University Board of Trustees Committee of the Whole, Video Recording June 17th, 2024 Pt. 1
No full textInvestigating the Motivational Differences for Healthy Eating in Men and Women
Get PDFThe study aimed to measure the differing levels of intrinsic and extrinsic motivation for healthy eating behaviors in men and women. Through social media outreach, a sample of 57 participants (n=57), aged 18-69, living across the United States, primarily in the midwestern area, completed an online survey. The Motivation for Healthy Eating Scale (MHES) assessed different subgroups of internal and external motivation for healthy eating. Five of the six subgroups were used in the online survey sent to participants (intrinsic motivation, integrated regulation, identified regulation, introjected regulation, and external regulation). An independent samples t-test was performed to assess the differing MHES intrinsic and extrinsic motivation results between the male and female participants. Results indicated no statistical significance between gender in four of the five MHES subgroups: intrinsic motivation (p = .163), integrated regulation (p = .866), identified regulation (p = .309), and introjected regulation (p = .151). Extrinsic regulation was the only subgroup with significant results (p = .035). A paired samples t-test was also performed to evaluate the MHES results within men and women separately. Both tests indicated no statistical significance between the differing types of motivation in men and women (p = .122, p = .140, respectively). The present study suggests that there are mostly no significant motivational differences for healthy eating between and within men and women. However, the study does suggest that there is significance in differing levels of external motivation between men and women for healthy eating. Further studies conducted on this subject should consider focusing on a young adult population in order to account for social media internal and external influences on healthy eating motivation
A Geriatric Interprofessional Education Workshop: A Mixed Method Study
Get PDFInterprofessional healthcare teams are essential for meeting the needs of our older population. To prepare for interprofessional collaboration, educational programs should offer opportunities for interprofessional education. We created a four-hour geriatric interprofessional workshop addressing cognition, macular degeneration, dysphagia, home safety, fall risk, and pharmacotherapy to examine student perceptions of working in interprofessional teams and their attitudes toward geriatric patients. Participants included 189 second-year medical (MD) students, 41 first-year occupational therapy (OT) students, and 36 second-year physical therapy (PT) students. We utilized a sequential explanatory mixed-method approach. Data analysis comprised descriptive statistics, paired t-tests for the pre/post surveys, and inductive content analysis to examine the focus group transcript. One hundred twenty students completed the pre/post surveys. Results demonstrated significant increases in students’ perceptions of the value of interprofessional teams (pp=.013) and post-hoc analysis (p=.016). The themes expressed by participants during the focus group included collaboration, team roles, the value of IPE, and recommendations for future IPE activities. Our findings demonstrate that our geriatric educational workshop significantly improved students’ perception of the value of interprofessional teams
Using Cyber Social Research to Create an Effective AI Exercise
No full textThe new educational revolution is here with artificial intelligence (AI), especially as it relates to text-generating chatbots, like ChatGPT, which will change almost every aspect of how we teach, and especially how we create home assignments. As such, professors are wise to integrate ChatGPT into their coursework to give students a jumpstart on grasping this technology. But instructors want to do so in a way that is relatively easy to not overwhelm students, especially those who aren’t used to being early adopters. To achieve this goal, this presentation introduces you to a useful AI exercise that manifested from the principles from cyber social research, and its effectiveness is measured both quantitatively and qualitatively
