Literature DB >> 28577816

Academic and Demographic Predictors of NCLEX-RN Pass Rates in First- and Second-Degree Accelerated BSN Programs.

Mahmoud A Kaddoura1, Elizabeth P Flint2, Olga Van Dyke3, Qing Yang4, Li-Chi Chiang5.   

Abstract

Relatively few studies have addressed predictors of first-attempt outcomes (pass-fail) on the National Council Licensure Examination-Registered Nurses (NCLEX-RN) for accelerated BSN programs. The purpose of this study was to compare potential predictors of NCLEX outcomes in graduates of first-degree accelerated (FDA; n=62) and second-degree accelerated (SDA; n=173) BSN programs sharing a common nursing curriculum. In this retrospective study, bivariate analyses and multiple logistic regression assessed significance of selected demographic and academic characteristics as predictors of NCLEX-RN outcomes. FDA graduates were more likely than SDA graduates to fail the NCLEX-RN (P=.0013). FDA graduates were more likely to speak English as a second or additional language (P<.0001), have lower end-of-program GPA and HESI Exit Exam scores (both P<.0001), and have a higher proportions of grades ≤ C (P=.0023). All four variables were significant predictors of NCLEX-RN outcomes within both FDA and SDA programs. The only significant predictors in adjusted logistic regression of NCLEX-RN outcome for the pooled FDA+SDA graduate sample were proportion of grades ≤ C (a predictor of NCLEX-RN failure) and HESI Exit Exam score (a predictor of passing NCLEX-RN). Grades of C or lower on any course may indicate inadequate mastery of critical NCLEX-RN content and increased risk of NCLEX-RN failure.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Baccalaureate; HESI exit exam; Low course grades; NCLEX-RN; Nursing education; Predictor variables

Mesh:

Year:  2016        PMID: 28577816     DOI: 10.1016/j.profnurs.2016.09.005

Source DB:  PubMed          Journal:  J Prof Nurs        ISSN: 8755-7223            Impact factor:   2.104


  2 in total

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2.  Assessment of determinants predicting success on the Saudi Nursing Licensure Examination by employing artificial neural network.

Authors:  Vincent Edward Butcon; Eddieson Pasay-An; Maria Charito Laarni Indonto; Liza Villacorte; Jupiter Cajigal
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  2 in total

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