Welcome back! In our last issue we began this series on meeting the ARC-PA’s requirement of 85% or better first-time PANCE pass rates, and how to complete the necessary report when this benchmark is not met.  The ARC-PA expects ten separate elements to be examined in their report, so today we will look in depth at the first of ten. While this information is valuable in determining where a PA program needs improvement, it is equally, if not more valuable for establishing an ongoing assessment process that prevents future pass-rates from dropping below benchmark.

Admissions Criteria as Predictors of Success

A PA program must examine the relationship between admissions variables and student success on the PANCE. This includes looking at both undergraduate GPA, and the science GPA. This can be examined from a descriptive viewpoint including the relationship specifically for those that failed the PANCE as well as from a parametric viewpoint looking at the variance and strength of predictability based upon the admissions data. 

I have looked at multiple longitudinal studies of PA cohorts to examine the correlation between admissions criteria and PANCE performance. The results provide some interesting insights; here I will share the results of two such studies.

Case B Study

This case looked at a 2021 cohort. Linear multiple regression analysis was conducted with PANCE score as the predicted variable, and all prerequisite admission variables (Science GPA, Overall GPA, and Admission Committee Total Score) as predictor variables. 

Now, in this instance, none of the variables were statistically significant predictors of PANCE score. The difference between the “pass” and “fail” groups were not significant to draw any conclusions. However, this does not mean we have no results to build upon.

As a result of these findings: 

1. The program has incorporated a pre-matriculation program for students coming into the academic program effective for the class of 2024. This is designed to provide science foundation knowledge and student skill development.

2. Because including students who are less competitive is part of the mission of this program, the pre matriculation program and the incorporation of a student coaching program will address students at-risk early in the academic program.

3. The program is piloting the use of the PA-CAT for students who have been accepted and matriculate into the program. This will provide some additional insights about the science aptitude. This action plan was motivated by the lack of discernible difference in undergraduate variables to identify students who may necessitate additional student success coaching and monitoring prior to experiencing academic difficulty

4. For the incoming class of 2024 the program will exercise more discrimination when looking at students with lower threshold science GPA’s. Seven of eleven graduates who failed PANCE had an overall science GPA > 3.30. This will be set as a benchmark for incoming class of 2024 until more data can be analyzed longitudinally

Case D Study

In this analysis, we did find a correlation between undergraduate grades and the PANCE.  

The regression model with just Undergrad GPA as the predictor variable was statistically significant (p=0.033) and explained about 11% of the variance in PANCE scores. The regression model with just Science GPA as the predictor variable was statistically significant (p=0.033) and explained about 11% of the variance in PANCE scores. 

The descriptive analysis and parametric analysis demonstrated a significant relationship between undergraduate GPA, science GPA and performance on the PANCE. 

These results meant that, within this PA program, applicants considered for admission with science GPA's and undergraduate GPAs below 3.40 should be closely watched during the first semester for any evidence of academic difficulty.