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.
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.
This study compared predictive logistic regression with traditional rational judgment models to classify potential nursing school applicants.
A higher number of Hispanic and black prenursing students were identified for potential upper-level nursing program admission.
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.
Author Affiliations: Coordinator, RN BS/MS Program, and Assistant Professor (Dr Stankus), College of Nursing; Associate Provost of Institutional Research & Improvement (Dr Hamner); Director of Analytics (Dr Stankey); and Associate Dean for Research and Clinical Scholarship/Professor (Dr Mancuso), College of Nursing, Texas Woman's University, Denton.
The authors declare no conflicts of interest.
Funding was provided by Health Resources and Services Administration Scholarships for Health Professions Students from Disadvantaged Backgrounds (award CFDA #93.925).
Correspondence: Dr Stankus, College of Nursing, Texas Woman's University, 1216 Oakland St, ASB 124, PO Box 425498, Denton, TX 76204 (email@example.com).
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.nurseeducatoronline.com).
Accepted for publication: June 1, 2018
Published ahead of print: July 26, 2018