Document Type : Original Article
Authors
- . Vincent Edward Butcon
- . Eddieson Pasay‑An
- . Maria Charito Laarni Indonto
- . Liza Villacorte
- . Jupiter Cajigal
Abstract
BACKGROUND: 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).
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
Keywords
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