Investigating the Integration of Student Learning Resources in Preparation for the NCLEX-RN: Phase One of a Canadian Two-Phase Multi-Site Study
Bibliographic record
Abstract
Evidence-informed education practices are critical in determining effective student preparatory learning resources for the NCLEX-RN examination. Standardized testing in nursing education programs has been demonstrated to increase students NCLEX-RN success. A widely researched assessment tool for predicting NCLEX-RN examination outcomes is the HESITM RN Exit Exams. The HESI Exit Exam (E2) was determined to be between 93.36% and 99.16% accurate in predicting NCLEX-RN success (N = 49,115) with samples derived from various nursing programs throughout the United States. Purpose: This two-phase, multi-site ex-post facto study was to investigate NCLEX-RN Student Preparatory Learning Resources within the Canadian context. Phase One, which is reported here, was to determine if there was a relationship between student HESITM RN Exit and Computer Adaptive Testing (CAT) Exam scores, student grade point average (GPA), and the time lag between graduation and writing the National Licensure exam, and student outcome on the NCLEX-RN exam. Procedure: New nursing graduates were emailed study information and asked to provide their consent for the use of their student data (GPA, HESITM Exit and CAT Exam scores) for research purposes and to request that they self-report (via a password protected secure email address created for this study) their NCLEX-RN Licensure exam date and result (pass/fail) of their first exam writing. Results: Among a convenience sample of 117 new nursing alumni (graduates of 2015) from three universities in Nova Scotia, we found statistically significant mean differences in HESITM RN Exit Exam Version 1, Version 2, and CAT scores among those students that were successful on the NCLEX-RN exam versus those students that were not successful on their first writing of the NCLEX-RN exam. There was an inverse statistically significant relationship between time lag and NCLEX-RN outcome indicating that the longer the time period from graduation to writing, the less likely that the student will be successful. We found no relationship between student GPA and NCLEX-RN outcome. Discussion: Phase One results of this study suggest that there are differences in HESITM RN Exit exam and CAT scores among those students who were successful on the first write of NCLEX-RN exam versus those students who were not successful.. Although HESITM exams are just one type of the many available nursing resources to assist students to prepare for writing the National Licensure examination, our findings are significant and warrant Canadian nurse educators’ attention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".