title: Development of a prediction model for early diagnosis of not passing the national council of licensure examination for registered associate degree nurses.
abstract: Passing the national licensure examination for registered nurses (NCLEX-RN) in the US is a critical outcome of the nursing program. Research has been conducted to identify which nursing students are at risk for not passing the NCLEX-RN test. The purpose of this study was to investigate whether any of several student covariates can be used to accurately identify associate in science in nursing (ASN) students that are at-risk for failing the NCLEX-RN test. Covariates included in the study were demographics, students' pre-admission grade point average (GPA), the scores of test of essential skills (TEAS), and the assessment technologies institute® (ATI)'s comprehensive scores for a pre-RN examination test. Chi-squared automatic interaction detection, or CHAID analysis, was used to develop the model. One covariate, ATI comprehensive test scores, was found to accurately identify all at-risk ASN students. The model explained that students identified as "at-risk" had a failure rate nearly two-and-a-half times as high as the general population.
authors.affiliation: Utah Valley University, Orem, Utah, USA.
authors.forename: Hsiu-Chin
authors.initials: HC
authors.lastname: Chen
authors.affiliation: Utah Valley University, Orem, Utah, USA.
authors.forename: Erik L
authors.initials: EL
authors.lastname: Heiny
authors.affiliation: Utah Valley University, Orem, Utah, USA.
authors.forename: Chia-Hsuan
authors.initials: CH
authors.lastname: Lin
mesh_terms: D000293:Adolescent; D000328:Adult; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004507:Education, Nursing, Associate / Q000145:classification* / Q000706:statistics & numerical data*; D004521:Educational Measurement / Q000379:methods*; D005260:Female; D006801:Humans; D008004:Licensure / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D013338:Students, Nursing / Q000145:classification* / Q000706:statistics & numerical data; D055815:Young Adult