{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:14:36Z","timestamp":1766268876737,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>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&amp;reg; (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 &amp;ldquo;at-risk&amp;rdquo; had a failure rate nearly two-and-a-half times as high as the general population.<\/jats:p>","DOI":"10.3233\/978-1-61499-415-2-271","type":"book-chapter","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T23:19:12Z","timestamp":1740266352000},"source":"Crossref","is-referenced-by-count":1,"title":["Development of A Prediction Model for Early Diagnosis of Not Passing the National Council of Licensure Examination for Registered Associate Degree Nurses"],"prefix":"10.3233","author":[{"family":"Chen Hsiu-Chin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Heiny Erik L.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Lin Chia-Hsuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Nursing Informatics 2014"],"original-title":[],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T23:43:06Z","timestamp":1740267786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0926-9630&volume=201&spage=271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-415-2-271","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}