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<p>This study investigates the impact of prerequisite science courses on student outcomes in a physician assistant (PA) training program. Given the conflicting literature on the necessity of such coursework and the variation in required courses across PA programs, this study aimed to assess whether prerequisite courses are essential for success. One PA program eliminated prerequisite coursework, allowing analysis of impacts on student outcomes. Data from 2018–2021 Centralized Application Service for Physician Assistants (CASPA) records and student performance metrics were analyzed. Stratification analysis and Pearson correlation coefficients were used to assess the relationship between the number of science credit hours and clinical year variables. Results indicate no consistent correlation between completed prerequisite credit hours and student performance on various assessments. Even applicants with minimal (0–1) credit hours performed similarly to those with extensive (&gt; 15) credit hours. There was no statistically significant correlation between prerequisite courses and Physician Assistant National Certifying Exam (PANCE) scores. Additionally, the study found no significant relationship between the nationally benchmarked Physician Assistant Clinical Knowledge Rating and Assessment Tool (patterned off the PANCE) scores and the number of physiology credit hours, but a notable inverse relationship between genetics credit hours and a nationally benchmarked summative examination (End of Curriculum Exam (ECOE)) performance. The analysis revealed that variations in prerequisite coursework did not consistently correlate with student performance across assessments, casting doubt on the necessity of specific science courses for PA program success. However, limitations including fewer candidates with prerequisite coursework requirements, and variability in assessment tools, may influence the generalizability of these results.</p>

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Correlation of Physician Assistant Prerequisite Coursework and Student Success

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月15日

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  • Sarah Neguse,
  • Darcy Solanyk,
  • Jennifer L. Hellier,
  • David Holmerud,
  • Scott Massey,
  • Cathy Ruff

摘要

This study investigates the impact of prerequisite science courses on student outcomes in a physician assistant (PA) training program. Given the conflicting literature on the necessity of such coursework and the variation in required courses across PA programs, this study aimed to assess whether prerequisite courses are essential for success. One PA program eliminated prerequisite coursework, allowing analysis of impacts on student outcomes. Data from 2018–2021 Centralized Application Service for Physician Assistants (CASPA) records and student performance metrics were analyzed. Stratification analysis and Pearson correlation coefficients were used to assess the relationship between the number of science credit hours and clinical year variables. Results indicate no consistent correlation between completed prerequisite credit hours and student performance on various assessments. Even applicants with minimal (0–1) credit hours performed similarly to those with extensive (> 15) credit hours. There was no statistically significant correlation between prerequisite courses and Physician Assistant National Certifying Exam (PANCE) scores. Additionally, the study found no significant relationship between the nationally benchmarked Physician Assistant Clinical Knowledge Rating and Assessment Tool (patterned off the PANCE) scores and the number of physiology credit hours, but a notable inverse relationship between genetics credit hours and a nationally benchmarked summative examination (End of Curriculum Exam (ECOE)) performance. The analysis revealed that variations in prerequisite coursework did not consistently correlate with student performance across assessments, casting doubt on the necessity of specific science courses for PA program success. However, limitations including fewer candidates with prerequisite coursework requirements, and variability in assessment tools, may influence the generalizability of these results.