Physician Assistant Applicant Pool: The First 50 Years.

The physician assistant (PA) profession's first attempt to characterize the applicant pool for PA education began with publication of the first Annual Report on Physician Assistant Educational Programs in the United States in 1985. The methodology used in the report was limited, however, in identifying the number of unique applicants to PA programs. Collecting accurate and reliable data on the profession's applicant pool was the primary motivator leading to initiation of the Central Application Service for Physician Assistants (CASPA) in 2001. In the past 15 years, CASPA has provided increasingly valuable data on the profession's applicant pool, allowing for accurate tracking and analysis of trends in the growth and changing demographics of those applying to PA educational programs. This special report presents a unique analysis of CASPA data that relates the competitiveness of entry into PA programs with that experienced by our colleagues in medicine, for both Doctor of Medicine (MD) and Doctor or Osteopathic Medicine (DO) schools. We present data reflecting the most notable changing demographics of the profession's applicant and matriculant pools in sex, age, grade point average, and health care experience. We use aggregate data of self-identified race descriptors to compare the contributions of PA, medical, and osteopathic medicine schools to the improvement of diversity within the health professions. To date, the applicant pool of PA programs seems to have kept pace with the expansion of existing programs and the development of new programs. This article poses serious questions for the profession to ponder, as the demographics of those entering PA education change and the number of PA graduates continues to grow.

Admission variables as predictors of PANCE scores in physician assistant programs: a comparison study across universities.

PURPOSE: The purpose of this study was to create a model of cognitive and noncognitive measures that could estimate the probability of achieving a given level of performance on the Physician Assistant National Certifying Examination (PANCE). METHODS: A retrospective records review of admissions information used by six universities was conducted to discover which factor had the most impact on the dependent variable of the PANCE score. Multiple predictors were measured: undergraduate grade point average (uGPA), graduate GPA, prerequisite grades, Graduate Record Exam (GRE)-verbal, GRE-quantitative, GRE combined, interview scores, years of health care experience, age, gender, and first-year scores on the Physician Assistant Clinical Knowledge Rating and Assessment Tool (PACKRAT). While PACKRAT scores are not applicable to admission selection, they are a strong midpoint predictor of PANCE performance. Multiple regression analysis was used to develop prediction equations. Expectancy tables were developed to provide estimation of PANCE performance, given the various score ranges on each of the predictor variables. RESULTS: Four predictors made a significant contribution to the final regression equation: GPA, GRE-verbal, GRE-quantitative, and PACKRAT scores. The PACKRAT scores were consistently the best predictors of performance on the PANCE. Each of these four predictors can be plugged into predictability tables to estimate the probability of achieving various score intervals on the PANCE. CONCLUSION: A model of equations and predictors can be used to project how successful a physician assistant (PA) graduate will be on PANCE performance. Years of health care experience, grades on prerequisites, and demographics were not significant predictors across programs but did have significance in certain individual institutions. Future research should examine which specific noncognitive traits measured in interviews can add value to predictability.