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Supplemental digital content is available in the text. 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. [ABSTRACT FROM AUTHOR] more...

Supplemental digital content is available in the text. Background: The traditional admission methodology used by many schools of nursing is the second-tier admission based on specified criteria. This method has been associated with a student body that is predominately female, white, and English speaking. Problem: Rational judgment modeling (points assigned to criteria) used to evaluate applicants for admission into prenursing programs potentially overlooks students who do not fit into the traditional model. Approach: This study compared predictive logistic regression with traditional rational judgment models to classify potential nursing school applicants. Outcomes: A higher number of Hispanic and black prenursing students were identified for potential upper-level nursing program admission. Conclusions: The use of logistic regression modeling can identify a more diverse student population. Advisors at both high school and university/college levels can use results of this model to help students determine their progress, identify academic weaknesses, and develop individual plans of action to help students successfully complete the prenursing curriculum requirements. [ABSTRACT FROM AUTHOR] more...