Canadian NCLEX-RN outcomes: A two-year cross-sectional exploratory study in Ontario
Bibliographic record
Abstract
Background/Objective: In January 2015, 10 of 12 registered nurse regulators in Canada began using the National Council Licensure Exam for Registered Nursing (NCLEX-RN) as the entry-to-practice examination. We examine the NCLEX-RN performance of BScN graduates from three sites of one program across the first 2 years of its use. We aim to investigate the relationship between undergraduate academic performance and NCLEX-RN performance, and to determine predictors of success and/or failure on the exam.Methods: A total of 215 graduates who wrote the 2015 or 2016 NCLEX-RN participated in the study. Course grades, final program percentage grade and GPAs, and students’ time to complete the program are examined against pass/fail performance on the NCLEX-RN. Student’s t test and Chi Square tests are used for comparative analysis. Logistic regression identified the odds ratio and associated 95% confidence interval for each one-unit increase in GPAs as a predictor of success.Results: Overall, 141 of 215 (66%) graduates passed NCLEX-RN and 74 (34%) failed, with no significant difference between the two years. Time to complete the program is significantly lower (p = .002) and graduating GPA is significantly higher (p < .001) among those who passed the NCLEX-RN compared to those who failed. With one exception, all course grades are significantly higher for students who passed, compared to those who failed. The odds of passing the NCLEX-RN increase by 10 (95% CI 4.5, 22.6) for each one-point increase in GPA. At a GPA of 4.0, zero failures on NCLEX-RN are observed.Conclusions: To our knowledge, this is the first Canadian systematic institutional based study examining the relationship between NCLEX-RN performance and academic factors. The study concludes high academic performance remains a strong predictor for NCLEX-RN success. Future, preferably multicenter larger studies, could further the understanding of the performance on this exam in Canada and support practices enhancing students’ success on 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".