Early Prediction Model for PANCE Success 0598-000212

Andrew R. Wyant, M.D., Assistant Professor Physician Assistant Studies Randa Remer, Ph.D., Assistant Dean of Admissions and Student Affairs Michelle Butina, Ph.D., Director of the Medical Laboratory Sciences Robert Cardom, M.S., Counseling Psychology Graduate Assistant

Overview

Purpose Method Results Conclusions

Purpose of the Study

• Historical context • Background studies

Historical Context

• …’the identification of students at risk of failing the PANCE is one of the largest challenges facing PA educators’….. S Massey

• Academic literature has called for “models of early prediction”

Historical context

Ensuring PANCE Success is vital to programs & students!

• Student debt & future employment • Program accreditation • Recruitment of future students

Historical Context

• Targeted Remediation • Conditional Admissions • Identification of learning barriers • Counseling • Academic Intervention

Power of Prediction:

Background Studies

Undergraduate Performance? •Undergraduate GPA (uGPA) & GRE scores

• Mixed results of uGPA & GRE scores to predict PANCE

Early Graduate Performance? •Year one graduate GPA (gGPA) & PANCE passage • Year one gGPA of < 3.0 associated with increased risk of PANCE failure (Ennulat C. 2003; Nilson W, APAP 2003)

Background Studies

• PACKRAT – Currently the most potent predictor

of PANCE success or failure – Explains score variance & likelihood

of passing PANCE • PACKRAT I score 118(52%) • PACKRAT II score 127 (56%) • PACKRAT II accounts for 58% of score

variance

Background Studies

• Combined MCQ exams from didactic year – Statistically, a weak predictor of ‘at-

risk’ status

• Summative MCQ + PACKRAT II – Strong correlation with predicting

PANCE success – 3 months prior to graduation

Background Studies

Summary

•Research has focused on two areas: • Admissions Criteria • Summative Testing / PACKRAT II + Cumulative MCQs

Hypotheses

• Admission criteria – GPA, KEY GPA, and GRE will predict

positive PANCE success • Foundational courses will predict PANCE

success – Pharmacology – Physiology – Anatomy

• Admissions criteria will predict success in foundational courses and ultimate success on the PANCE

Hypothesis Summary

Method

• Pre-admission predictors • Graduate program predictors – PACKRAT – Summative – Foundational Courses

• PANCE Success – Passing the exam (P/F) – Score variance

Results

• Path Analysis – Model fit adequate (CFI=0.97,

TLI=0.91, RMSEA = .08) explains 53% of the variance in PANCE

• Standardized Regression – Foundational Courses • Strong predictor of PANCE performance

– (B= 0.72, p < .001) – uGPA, kGPA, & mGRE • Moderate prediction of Foundational

Courses

Title

Table 1. Standardized Regression Estimates from the Path Analysis

IV DV Direct Indirect Total

uGPA PANCE 0.14 .202 .341

kGPA PANCE -.141 .14 -.001

vGRE PANCE .116 .046 .163

mGRE PANCE -.082 .155 .073

FC PANCE .715

uGPA FC .282

kGPA FC .196

vGRE FC .065

mGRE FC .216

Conclusions

• Foundational science courses are fundamental to the students understanding of clinical science and critical reasoning.

• Math GRE and overall uGPA are reasonable predictors of foundational science courses which provides a strong basis for admissions selection.

• Foundational coursework identifies at-risk students for interventions.

Future Directions

• Interventions – Tutoring – Mindfulness Seminars – Conditional admissions criteria – Success seminars prior to matriculation – Faculty mentoring – Pre-science modular course prior to

matriculation • Review the impact of the interventions on

foundational course success and PANCE outcomes. • Review patient contact and shadowing prior to

admissions to determine impact on foundational course success and PANCE outcomes.