Artificial intelligence (AI) and generative artificial intelligence (GenAI) have gained increasing relevance in occupational therapy (OT) due to their potential to enhance clinical practice, optimize client care, and shape the future of OT education. Despite growing international evidence, literature addressing AI use in OT remains limited in the Philippine context. This Special Collection on AI in Occupational Therapy seeks to address this gap by examining the perspectives and experiences of key stakeholders across OT education and practice through a stakeholder-informed qualitative approach. Using interviews and focus group discussions, insights from school administrators, OT educators, interns, students, and clinicians are gathered to explore their attitudes, concerns, and lived experiences related to AI use in occupational therapy. These multi-perspective findings aim to inform the development of contextually grounded frameworks, institutional policies, and evidence-based programs that support ethical, sustainable, and meaningful integration of AI in OT education and practice within the local setting.

Human ; Artificial Intelligence ; Occupational Therapy ; Philippines

Background: Sleep is an essential occupation for students. It affects one's neurocognitive functions and psychomotor performance, playing a significant role in academic performance, health, and well-being. This study aims to describe the sleep quality and sleep patterns among undergraduate occupational therapy (OT) students at the University of Santo Tomas (UST) during the COVID-19 pandemic. Methods: This descriptive cross-sectional study utilized a record review based on existing data from the UST OT Department's Student Life Survey 2021 database. A total of 205 students from different year levels and academic cohorts of the academic year 2021-2022 participated in the survey, which included the Pittsburgh Sleep Quality Index (PSQI). The data with valid responses (n=204) was analyzed using the measures of central tendency and dispersion, such as mean and standard deviation. Results: Data analysis revealed that 95.59% (n=195) of student respondents had significantly poor sleep quality, while 4.41% (n=9) had good sleep quality. Students in the fifth-year level had the greatest sleep dysfunction as opposed to those in the third-year level who had little sleep dysfunction. Sleep duration and daytime dysfunction received the highest individual scores across all year levels. Conclusion Having poor sleep quality is a common occurrence among undergraduate OT students in UST during the COVID-19 pandemic. Sleep pattern trends suggest the need for school administrators and educators to create measures to mitigate possible negative effects on their student’s academic performance, health, and overall quality of life, especially during public health emergencies, calamities, and disasters.
Sleep Quality ; COVID-19