Identifying Indicators of National Council Licensure Examination for Registered Nurses (NCLEX-RN) Success in Nursing Graduates in Newfoundland & Labrador
Bibliographic record
Abstract
In Canada in 2015, the pass rates on the National Council Licensure Examination (NCLEX-RN) were considerably lower than pass rates on the Canadian Registered Nurse Examination (CRNE) causing nurse educators to express concern regarding the NCLEX-RN. The purpose of this study was to examine the relationship between candidate variables (e. g. academic performance, demographics) on their NCLEX-RN outcome (pass/fail). A cross-sectional data linkage design was employed using multiple sources of data on nursing graduates who wrote the NCLEX-RN in 2015, 2016 and 2017 (n = 259). Results showed that fewer questions answered on the NCLEX-RN and higher grades in various nursing courses (e. g. Introduction to Nursing, Statistics) predicted higher odds of passing the NCLEX-RN. To improve pass rates, nurse educators must integrate diverse methods of testing into existing curricula that mimic the NCLEX-RN exam, specifically computer adaptive exams. Further research is needed to determine other possible challenges for countries considering adopting the NCLEX-RN.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".