Predictors of Success in the NCLEX-RN for Canadian Graduates
Bibliographic record
Abstract
The National Council Licensure Examination for Registered Nurses (NCLEX-RN) has been the nursing licensure exam in most Canadian jurisdictions since 2015. Nursing faculty across the country have invested considerable effort into understanding the NCLEX-RN, so they could help to prepare students to be successful in the exam. A retrospective study was conducted at one Canadian university to identify predictors of success on the NCLEX-RN. Findings revealed that the strongest predictors of success were a grade point average of >3.5 and a course grade in the community development course. The strong predictive value of the community development course was unexpected, and this suggests that content specifically related to acute care may not play as heavy a role in the NCLEX-RN outcome as previously expected. It is possible that students' higher levels of cognitive abilities, such as application, analysis and synthesis of nursing knowledge, play a larger role in the exam outcome than content-specific knowledge.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".