The relation between standardized test scores and NCLEX-RN failure
Bibliographic record
Abstract
First time pass rates on the National Council Licensure Examination – Registered Nurses (NCLEX-RN) are the desired outcome of Schools of Nursing across the United States. The purpose of this study was to determine if there was a relation between standardized testing scores and NCLEX-RN failure among students in a baccalaureate nursing program. This study utilized a retrospective correlational design to identify relations between the dependent variables NCLEX-RN failure and the independent variables. The 16 Independent Variables were: Fundamentals of Nursing Practice and Repeat, Adult Health Nursing and Repeat, Mental Health Nursing and Repeat, Pediatric Nursing and repeat, Obstetrical Nursing and Repeat, Community Health Nursing and Repeat, RN Exit Exam and Repeat, and Pharmacology and Pharmacology Repeat. The statistical tests utilized for data analysis were: logistic regression, multiple logistic regression model, and Pearson’s χ2 cross-tabulations. Significant findings identified students “at risk” for NCLEX-RN failure. Faculty intervention early on and throughout the students’ nursing program will help improve student outcomes, NCLEX-RN success, and ultimately helping to alleviate the nursing shortage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".