Journal of Education and Health Promotion

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

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

Determinants
examination
neural network analysis
nursing
nursing licensure
success
  1. National Council of State Boards of Nursing. A Global Profile
    of Nursing Regulation, Education, and Practice. J Nurs Regul
    2020;10:1‑116.
    2. Oducado R, Cendana D, Belo‑Delariarte R. Institutional
    competency assessment and other factors influencing the nurse
    licensure examination. Int J Sci Technol Res 2019;8:268‑70.
  2. 3. Saudi Commission for Health Specialties. Saudi Nursing
    Licensing Exam Applicant Guide. 2019. Available from: http://
    www.scfhs.org.sa. [Last accessed on 2020 Jan 12].
    4. National Council of State Boards of Nursing. 2020 NCLEX pass
    rates; 2020. Available form: https://www.ncsbn.org/14664.htm.
    [Last accessed on 2020 Feb 06].
    5. College of Licensed Practical Nurses of Alberta. National Pass
    Rate Average CPRNE; 2020. Available from: https://www.clpna.
    com/education/practical‑nurse‑program‑cpnre‑results/.[Last
    accessed on 2020 Jan 29].
    6. Philippine Regulatory Commission. Nursing Board Exam Passers;
    2019. Available from: http://www.prcboard.com. [Last accessed
    on 2020 Feb 15].
    7. Saudi Commission for Health Specialties. Statistics of the
    performance of college graduates in the SNLE; 2019. Available
    from: http://www.scfhs.org.sa. [Last accessed on 2019 Dec 30].
    8. Johnson T, Sanderson B, Wang CH, Parker F. Factors associated
    with first‑time NCLEX‑RN success: A descriptive research study.
    J Nurs Educ 2017;56:542‑5.
    9. Havrilla E, Zbegner D, Victor J. Exploring Predictors of
    NCLEX‑RN Success: One School’s Search for Excellence. J Nurs
    Educ 2018;57:554‑6.
    10. Eddy LL, Epeneter BJ. The NCLEX‑RN experience: Qualitative
    interviews with graduates of a baccalaureate nursing program.
    J Nurs Educ 2002;41:273‑8.
    11. Bautista J, Ducanes G, David C. Quality of nursing schools in
    the Philippines: Trends and evidence from the 2010‑2016 nurse
    licensure examination results. Nurs Outlook 2019;67:259‑69.
    12. Nyangena E, Getanda A, Ngugi S. Factors influencing success
    of bachelor of science in nursing graduates in nursing council
    of Kenya licensure examinations. Baraton Interdiscip Res J
    2013;3:11‑21.
    13. Belo‑Delariarte R, Oducado R, Penuela A. Terminal assessment
    of core nursing knowledge in a state university. Asia Pac J
    Multidiscip Res 2018;2:10‑7.
    14. Ong M, Palompon D, Banico L. Predictors of nurses’ licensure
    examination performance of graduates in Cebu Normal
    University, Philippines. Asia J Health 2012;2(1):130‑41.
    15. Amankwaa I, Agyemang‑Dankwah A, Boateng D. Previous
    education, sociodemographic characteristics, and nursing
    cumulative grade point average as predictors of success in nursing
    licensure examinations. Nurs Res Pract 2015;2015:682479.
    16. Jeffrey P, Harris R, Sherman J. Quality improvement: A practical
    nursing program’s admission test. Nurse Educ Today
    2019;73:65‑70.
    17. Pike A, Lukewich J, Wells J, Kirkland MC, Manuel M,
    Watkins K, et al. Identifying indicators of National Council
    Licensure Examination for Registered Nurses (NCLEX‑RN)
    success in nursing graduates in newfoundland and labrador. Int
    J Nurs Educ Scholarsh 2019;1:1‑10.
    18. Hobbins B, Bradley P. Developing a prelicensure exam for Canada:
    An international collaboration. J Prof Nurs 2013;29:48‑52.
    19. ChukwuS, NwachukwuA. Analysis of some meteorological parameters
    using artificial neural network method for Makurdi, Nigeria. Afr J
    Environ Sci Technol 2012;6:182‑8.
    20. LeCunY, BengioY, HintonG. Deep learning. Nature 2015;521:436‑44.
    21. Zacharis N. Predicting student academic performance in blended
    learning using artificial neural networks. Int J Artificial Intell Appl
    2016;7:17‑29.
    22. HindererK, DiBartoloM, WalshC. HESI admission assessment(A2)
    examination scores, program progression, and NCLEX‑RN success in
    baccalaureate nursing: An exploratory study of dependable academic
    indicators of success. J Prof Nurs 2014;30:436‑42.
    23. SimonE, McGinnissS, KraussB. Predictor variables for NCLEX‑RN
    readiness exam performance. Nurs Educ Perspect 2013;34:18‑24.
    24. Lockie N, Van Lanen R, McGannon T. Educational implications
    of nursing students’ learning styles, success in chemistry, and
    supplemental instruction participation on national council licensure
    examination‑registered nurse performance. JProf Nurs 2013;29:49‑58.
    25. Rowland J. Admission Criteria, Program Outcomes, and
    NCLEX‑RN® Success in Second Degree Students. ProQuest
    Dissertations Publishing, 3592021; 2014. Available from: https://
    search.proquest.com/docview/1435629681?accountid=35493.
    [Last accessed on 2020 Jan 04].
    26. Trofino R. Relationship of associate degree nursing program
    criteria with NCLEX‑RN success: What are the best predictors
    in a nursing program of passing the NCLEX‑RN the first time?
    Teach Learn Nurs 2013;8:4‑12.
    27. KaddouraMA, FlintEP, Van DykeO, YangQ, ChiangLC. Academic
    and demographic predictors of NCLEX‑RN Pass rates in first‑ and
    second‑degree accelerated BSN programs. JProf Nurs 2017;33:229‑40.
    28. ChenH, BennettS. Decision‑tree analysis for predicting first‑time pass/
    fail rates for the NCLEX‑RN in associate degree nursing students. JNurs
    Educ 2016;55:454‑7.
    29. CovellC, PrimeauM, KilpatrickK, St‑PierreI. Internationally educated
    nurses in Canada: Predictors of workforce integration. Hum Resour
    Health 2017;15:1‑16.
    30. Lown SG, Hawkins LA. Learning style as a predictor of first‑time
    NCLEX‑RN success: Implications for nurse educators. Nurse Educ
    2017;42:181‑5.
Journal of Education and Health Promotion
Volume 11, Issue 9
October 2021
Pages 1-6

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