A Predictive Admissions MODEL to Ensure Diversity in PTA Programs
Purpose/Hypothesis: Student success in Physical Therapist Assistance (PTA) programs and passage of the National Physical Therapy Exam (NPTE), is required for accreditation by the Commission on Accreditation of Physical Therapy (CAPTE). CAPTE and the American Physical Therapy Association (APTA) formally state the need to diversify the profession. A predictive admissions model for student success that supports the diversity in PTA programs will further the goal of using admission criteria that do not adversely affect student demographics while maintaining accreditation standards for student success. In order to create a predictive model, a study was developed to determine if predictors of student success using grades earned in anatomy, physiology and overall GPA for courses completed before admission would yield a significant correlation to NPTE passage, without negatively impacting program diversity. The results were utilized to develop a predictive admissions model for student success to support student completion of the program, successfully pass the NPTE, and enter the workforce, while advancing diversity within the field. Number of Subjects: 109 students Materials and Methods: To develop a predictive model for student success, the independent variables of grade in anatomy, grade in physiology, and overall GPA were analyzed in relation to the dependent variable of score on the NPTE. Correlation statistics were used to measure the relationships between variables and multiple regression analysis was used to examine how well the predictor variables predicted NPTE score and whether diversity was impacted. Results: The grade in anatomy and the overall GPA were significantly correlated with NPTE scores. The regression determined that anatomy had measurable predictability while the overall GPA had a significant impact on NPTE scores. Regression also demonstrated that there was no adverse impact on diversity with the use of anatomy grade and GPA as admission criteria. Conclusions: The development of this predictive admissions model demonstrates that any program can follow a similar process which supports the diversification of the profession and provides for student success in PTA programs. Each program must determine the appropriate use of prerequisites to determine the impact on diversity and success within the program. Impacted health care degree programs should routinely analyze their admission criteria to improve diversity in health care professions. Clinical Relevance: A predictive admissions model for student success that supports the diversity in PTA programs will further the goal of using admission criteria that do not adversely affect student demographics while maintaining accreditation standards for student success. Therefore, use of a predictive formula should be used repeatedly over time with the goal of admitting diverse populations of students with a high pass rate on the NPTE. The predictive formula answers some of the important questions regarding how to balance the paradox of using admission prerequisites and student diversity as well as improving student success outcomes in the important area of PTA and allied health education.