Analysis of student performance in gateway courses has been an important predictor of successful admission into upper-division nursing.
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.
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.
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.
Identification of primary/secondary gateway courses enhances successful matriculation and provides valuable information for advisors and curriculum planners for prenursing majors.
Author Affiliations: Coordinator of RN BS/MS Program and Assistant Professor, College of Nursing (Dr Stankus), Vice Provost for Institutional Research & Improvement (Dr Hamner), Director of Analytics (Dr Stankey), and Associate Dean for Research and Clinical Scholarship/Professor, College of Nursing (Dr Mancuso), Texas Woman's University, Denton.
The authors declare no conflicts of interest.
Correspondence: Dr Stankus, College of Nursing, Texas Woman's University, 1216 Oakland St, ASB 124, PO Box 425498, Denton, TX 76204 (email@example.com).
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Accepted for publication: June 10, 2019
Published ahead of print: July 16, 2019
Cite this article as: Stankus J-A, Hamner M, Stankey M, Mancuso P. A Bayesian approach to gateway course identification with implications for the prenursing curriculum. Nurse Educ. 2019; doi: 10.1097/NNE.0000000000000728. [Epub ahead of print]