A Bayesian Approach to Gateway Course Identification With Implications for the Prenursing Curriculum

BACKGROUND: Analysis of student performance in gateway courses has been an important predictor of successful admission into upper-division nursing. PURPOSE: The aim was to explore the utility of a Bayesian statistical framework for determining threshold grades in prenursing courses that serve as gateways for successful admission into upper-division nursing programs. METHODS: Records of 3500 prenursing students who entered the prenursing program of a midsized public university during the past decade were analyzed. The Bayesian framework was used to incorporate conditional probabilistic concepts of sensitivity and specificity to calculate gateway impact of various grade level cutoffs on successful upper-division nursing admission. RESULTS: Identification, sequencing, and combination of grades attained in these gateway courses revealed different pathways to successful admission into upper-division nursing based on first-semester grade point average and ethnicity. CONCLUSIONS: Identification of primary/secondary gateway courses enhances successful matriculation and provides valuable information for advisors and curriculum planners for prenursing majors.