The Effects of a Pass-Fail Curriculum on a Physician Assistant Program.

Purpose: The grading scale for students in a physician assistant program of study is not standardized. Students may be evaluated on a traditional 5-tiered A to F scale or a pass-fail system. The decision to change from ordered grading to pass-fail at an established program in the southeast was done following a change in the affiliated School of Medicine.

Using Data Mining for the Early Identification of Struggling Learners in Physician Assistant Education.

Purpose: Despite the importance of early intervention and remediation, the relatively short duration of physician assistant education programs necessitates the importance of early identification of at-risk learners. This study sought to ascertain whether machine learning was more effective than logistic regression in predicting remediation status among students, using the limited set of data available before or immediately following the first semester of study as predictor variables and academic remediation as an outcome variable.

Methods: The analysis included one institution and student data from 177 graduates between 2017 and 2019.

PAEA End of Rotation™ Exams Accurately Identify Students at Risk of Physician Assistant National Certifying Exam Failure.

Purpose: Physician Assistant Education Association (PAEA) End of Rotation™ exams are used by programs across the country. However, little information exists on the predictive ability of the exams' scale scores and Physician Assistant National Certifying Exam (PANCE) performance. The purpose of this study was to evaluate End of Rotation exam scores and their relationship with poor PANCE performance (PPP).

Considerations Around Predicting Physician Assistant National Certifying Exam Scores From PACKRAT®: A Multiprogram Study.

Purpose: The Physician Assistant Clinical Knowledge Rating and Assessment Tool (PACKRAT®) is a known predictor of performance on the Physician Assistant National Certifying Exam (PANCE). It is unknown, however, whether these associations (1) vary across programs; (2) differ by PACKRAT metrics (first-year [PACKRAT 1], second-year [PACKRAT 2], and composite score [arithmetic mean of PACKRAT 1 and PACKRAT 2]); or (3) are modified by demographic or socioeconomic variables.

Methods: Linear and logistic hierarchical regression models (HRMs) were used to evaluate associations between PACKRAT metrics and (1) continuous PANCE scores and (2) odds of low PANCE performance (LPP), respectively.