Digital Commons Network^™

Artificial Intelligence: Current Uses In Occupational Therapy Education, Breanna Chycinski, Rebecca Tauber, Brina Crown, Caitlin Noble, Meredith Murphy

Journal of Occupational Therapy Education

Although artificial intelligence (AI) may be an advantageous tool to enhance learning, its societal impacts are still widely unexplored. This study examined how AI is utilized in occupational therapy master’s and doctoral educational programs by faculty and students, while also investigating how such programs are preparing students to employ AI in ethical and beneficial ways. Through the lens of the Person-Environment-Occupation (PEO) Model, this paper examined relationships among occupational therapy students, faculty, their educational environments, and AI. An online survey was distributed to faculty at accredited occupational therapy programs across the United States. Questions included topics such as classroom practices, …

Perceived Impacts Of Confucianism On Placement Experiences Of Hong Kong Pre-Registration Occupational Therapy Students In The United Kingdom, Hin Yuet Li, Sarah Louise Mcginley, Rachel Dadswell, Rachel Houweling

Journal of Occupational Therapy Education

This study explored the views of Hong Kong pre-registration occupational therapy (OT) students in the United Kingdom (UK) about how Chinese culture influenced their practice placement experiences. Students from diverse cultural backgrounds may encounter various challenges during OT placements, but little is known about how OT students from Hong Kong—where Confucianism traditionally shapes workplace expectations and the hierarchical teacher‑student relationship—experience placements in the UK. Two online focus groups to collect qualitative data were conducted in November 2022 with four participants who were UK-based OT students from Hong Kong (N=8 in total), two facilitators (OT students from Hong Kong, based in …

Nbcot First-Time Pass Rates At A Hispanic-Serving Institution: The Role Of Academic Metrics And Primary Language, Timothy Tu

Journal of Occupational Therapy Education

This retrospective study examined academic and demographic predictors of first-time success on the National Board for Certification in Occupational Therapy (NBCOT) exam among graduates (N = 82) from the classes of 2019 through 2022 at a Hispanic-Serving Institution. Despite demographic growth, Hispanic and linguistically diverse practitioners remain underrepresented in the profession. Analyzed variables included self-reported primary language, undergraduate GPA (UGPA), prerequisite GPA (PGPA), Graduate Record Examination (GRE) scores, and Occupational Therapy Knowledge Exam (OTKE) performance. Univariate and multivariate logistic regression analyses were conducted using SPSS 31. Three factors significantly predicted first-time NBCOT pass status: UGPA (p = .010), OTKE scores …

Streamlining Eligibility Assessment For Alzheimer’S Disease-Modifying Therapies: Prediction Of Mmse Scores Using The Digital Clock And Recall, Ali Jannati, Claudio Toro-Serey, Marissa Ciesla, Emma Chen, John Showalter, David Bates, Alvaro Pascual-Leone, Sean Tobyne

Faculty, Staff and Students Publications

           Introduction: The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks of perpetuating health disparities, given the test's known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR™)-a rapid, FDA-listed digital cognitive assessment-to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage.

Methods: We conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 (BH) study ( …

Le Parcours Des Travailleurs Immigrants Blessés Et Son Incidence Sur L’Alliance Thérapeutique : Une Étude De Cas Longitudinale En Contexte De Réadaptation Au Travail Au Québec, Maude Arsenault, Jessica Dubé, Daniel Côté

Études primaires

Cette étude explore l’incidence du parcours prélésionnel des travailleuses et travailleurs immigrants (TI) blessés sur l’alliance thérapeutique (AT) en réadaptation au travail. À partir d’une ethnographie longitudinale menée dans une clinique québécoise, sept trajectoires ont été analysées selon leurs dimensions migratoires, socioprofessionnelles et linguistiques. Les résultats montrent que le déclassement professionnel, la précarité de l’emploi, les barrières linguistiques et les expériences de racisme fragilisent la construction du lien de confiance et la définition partagée des objectifs de retour au travail. Lorsque l’emploi prélésionnel est perçu comme transitoire ou inadéquat, l’obligation institutionnelle d’y retourner crée une rupture symbolique qui entre en …

A Preliminary Investigation Of Research Collaboration Through Scientific Paper Co-Authorship In The Gulf Of Mexico, Juliet Vallejo, Evelyn Roozee, Dongkyu Kim, Andrew M Song, Christopher A Gabler, Jasper De Vries, Antonia Sohns, Gordon M Hickey, Owen Temby

Faculty, Staff and Student Publications

It is well known that publications with collaborators from external institutions increase citations. This effect scales with spatial distance. There are also many barriers to long-distance collaborations, including linguistic differences, funding constraints, and the incremental costs of remote collaboration. This paper uses the Gulf of Mexico as a case study to examine long-distance research collaboration because it consists of three countries with diverse development levels and two prominent diplomatic languages, within a singular regional ecosystem of tremendous natural and economic value. This paper uses bibliometric network analysis to examine scientific research article co-authorship in the Gulf of Mexico from 2000 …

Digital Communication Strategies For Culinary Medicine In Cancer Prevention And Survivorship: Information-Seeking Habits And Content Preferences Of Online Cooking Media Among Adults Participating In Four Cancer Prevention And Control Cohort Studies, Margaret Raber, Sara Fares, Maria Vazquez, Larkin Strong, Carrie Daniel-Macdougall, Lorna Mcneill, Karen Basen-Engquist

Faculty, Staff and Student Publications

Introduction

Poor diet and excess weight have been linked to increased risk for at least 13 types of cancer. Culinary medicine utilizes experiential cooking skill development to improve individuals’ capacity for healthy eating. Digital communication strategies offer pathways for scalable culinary medicine interventions, but little research has explored how online cooking tools could be leveraged for cancer prevention messaging. We conducted a cross-sectional survey study exploring online cooking information-seeking habits and content preferences among participants in four cancer prevention and control cohorts to inform future digital culinary medicine interventions.

Methods

A cross-sectional survey study was conducted with a convenience sample …

Knowledge Distillation And Dataset Distillation Of Large Language Models: Emerging Trends, Challenges, And Future Directions, Luyang Fang, Xiaowei Yu, Jiazhang Cai, Yongkai Chen, Shushan Wu, Zhengliang Liu, Zhenyuan Yang, Haoran Lu, Xilin Gong, Yufang Liu, Terry Ma, Wei Ruan, Ali Abbasi, Jing Zhang, Tao Wang, Ehsan Latif, Wei Liu, Wei Zhang, Soheil Kolouri, Xiaoming Zhai, Dajiang Zhu, Wenxuan Zhong, Tianming Liu, Ping Ma

Faculty, Staff and Student Publications

The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey provides a comprehensive analysis of two complementary paradigms: Knowledge Distillation (KD) and Dataset Distillation (DD), both aimed at compressing LLMs while preserving their advanced reasoning capabilities and linguistic diversity. We first examine key methodologies in KD, such as task-specific alignment, rationale-based training, and multi-teacher frameworks, alongside DD techniques that synthesize compact, high-impact datasets through optimization-based gradient matching, latent space regularization, and generative synthesis. Building on these foundations, we explore how integrating KD and DD can …