Early Prediction Model for PANCE Success
Transcript of Early Prediction Model for PANCE Success
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.