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

This study aims to use the artificial neural network as a novel approach to explore factors that determine and predict successful performance of nursing interns in Saudi Arabia on the Saudi Nursing Licensure Examination (SNLE).

The study employed a cross‑sectional, analytic approach. A total of 62 nursing interns were recruited by convenience sampling from the University of Hail to participate. Data collection was conducted from September to December 2019. Descriptive statistics were used to describe the demographic characteristics of the nursing interns and their responses regarding examination determinants. Neural network analysis was used to identify factors that are highly predictive of the success of the nursing interns on the SNLE.

Overall, the nursing interns were undecided (3.94 ± 0.14) about the influential factors determining their success. Their study hours (100%) and grade point average (GPA) (96.9%) were identified as strong determinants reflective of the tenacity and vigor of the nursing interns, based on the predictive power of the model. Meanwhile, age (45.7%), marital status (21.3%), gender (15.2%), and the type of academic program (5.9%) were considered the least important of the sociodemographic variables.

Exam preparation activities such as preparation programs, review classes, and exam simulations must be promoted and enhanced to increase the passing tendencies of the nursing interns in the SNLE. The GPA and increased study hours make the most significant contributions to success on the SNLE as compared to other variables such as age, gender, marital status, and the academic program. This study serves as a springboard for nursing educators and administrators in laying tailored strategies to strengthen the nurse interns’ GPA and time management.