Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 2 of 2

Full-Text Articles in Entire DC Network

The Environment Of Interprofessional Education In Graduate Education: Exploring Professional Programs Of Occupational Therapy, Physician Assistant, And Physical Therapy, Allison R. Kaczmarek Jan 2023

The Environment Of Interprofessional Education In Graduate Education: Exploring Professional Programs Of Occupational Therapy, Physician Assistant, And Physical Therapy, Allison R. Kaczmarek

Theses and Dissertations

Interprofessional education (IPE) is an educational approach of increasing popularity in professional schools for the preparation of a collaborative ready healthcare workforce. The accrediting bodies of professional education programs in occupational therapy (OT), physician assistant (PA), and physical therapy (PT) have incorporated standards for outcomes addressing IPE. Although they have endorsed the Health Professions Accreditors Collaborative (HPAC) consensus document on quality IPE, we do not have a contemporary snapshot of the IPE environments in the curriculum of their accredited programs. This dissertation, a collection of three distinct inquiries, has two aims: first, to provide a description of IPE as it …


Model-Based Imputation Of Below Detection Limit Missing Data And Group Selection In Bayesian Group Index Regression, Matthew Carli Jan 2023

Model-Based Imputation Of Below Detection Limit Missing Data And Group Selection In Bayesian Group Index Regression, Matthew Carli

Theses and Dissertations

Investigations into the association between chemical exposure and health outcomes are increasingly focused on the role of chemical mixtures, as opposed to individual chemicals. The analysis of chemical mixture data required the development of novel statistical methods, one of these being Bayesian group index regression. A statistical challenge common to all chemical mixture analyses is the ubiquitous presence of below detection limit (BDL) data. We propose an extension of Bayesian group index regression that treats both regression effects and missing BDL observations as parameters in a model estimated through a Markov Chain Monte Carlo algorithm that we refer to as …