Nursing school faculty are obligated to examine their programs and determine best practices to enable student success. Student success or failure in nursing programs has implications for the nursing workforce, and therefore safe, quality patient care (Quinn, Smolinski, & Peters, 2018). The Institute of Medicine’s Future of Nursing report (2011) emphasized the need for more baccalaureate-prepared nurses; growing the number of BSN-prepared nurses is a national mandate to help alleviate the shortage. For schools of nursing to efficiently accomplish the goal of producing more baccalaureate prepared nurses they need to determine ways to maximize the success of students in their undergraduate programs while not compromising academic rigor or patient safety (Glasgow, Dreher, & Schreiber, 2019). Studies have shown that pre-admission GPA, pre-nursing standardized tests, nursing school standardized exit exams, and nursing course grades are correlated with student success on passing the NCLEX-RN exam (Johnson, Sanderson, Wang, & Parker, 2017; Mathew & Aktan, 2018; Wambaugh, Eckfield, & Van Hofwegan, 2016). Thus, the purpose of this study was to examine the relationship between one standardized exam (RN Predictor) and other variables that have been shown to predict nursing student success. The following research questions were formulated: (1) Which nursing course grades are associated with RN Predictor scores? (2) Which pre-nursing screening tools and nursing courses explain the variance on RN Predictor scores? (3) Is there a difference in RN Predictor scores between traditional and accelerated BSN students?
Methods:
Using a cross sectional descriptive design, we conducted a secondary analysis of four-years of data describing characteristics of nursing students from admission to completion of a baccalaureate program. Only those with complete data were included in the analysis (N= 446). Institutional Review Board approval was obtained from the university for the secondary analysis. Both descriptive and inferential statistics were computed to answer the research questions.
Results:
Participants included both traditional BSN (N = 310) and accelerated BSN (N = 136) students. Pearson correlation was computed and all the major nursing courses significantly correlated with RN Predictor scores (p < 0.05). The results of multiple regression showed that the model containing seven pre-nursing screening tools from the holistic admission process explained 7.6% of variance in RN Predictor scores (F (7, 446) = 6. 24, p < 0.05). Multiple regression analysis results revealed that the major nursing courses explained 37.5% of variance in RN Predictor scores (F (7, 446) = 39.33, p < 0.05). Independent sample ‘t’ was calculated and the RN Predictor scores were found to be 3 points higher in ABSN group compared to BSN group ((F (3.4, 446) p < 0.05).
Conclusion:
The results of the study highlighted the evidence on factors that contribute to nursing student success and the RN Predictor exam results. The evidence will help the nursing faculties to identify the screening tools to select candidates for the nursing program and to identify at-risk students early and intervene to enhance student success in nursing courses, scores on standardized exams, and first time pass rates on the (NCLEX-RN).