Examining the relationships between NCLEX-RN performance and nursing student factors, including undergraduate nursing program performance: A systematic review
Bibliographic record
Abstract
The National Council Licensure Examination for Registered Nursing (NCLEX-RN) replaced the Canadian Registered Nursing Examination (CRNE) in January 2015 as part of the requirements for qualifying for registered nurse (RN) status in all but one Canadian province. This substantial change is likely to have a profound impact on Canadian undergraduate nursing degree programs practices. This systematic review aims to examine the evidence describing the relationship between undergraduate nursing program performance and NCLEX-RN performance by registered nursing candidates as well as other factors contributing to NCLEX-RN success. An initial search of CINAHL and Medline databases using key terms NCLEX and predict, publication dates between 1984-2015 revealed 46 articles of interest. 28 articles were examined for eligibility and 17 articles with clear evidence and descriptions were included. Thematic groupings of academic, cognitive, individual factors and models for successes are presented. The review showed academic factors are strong predictors for NCLEX-RN success. Cognitive factors, particularly critical thinking skills, are correlated with NCLEX-RN success. Stress and highly negative emotions inversely correlate with NCLEX-RN success. Speaking English as a first language showed high correlation with NCLEX success, while other non-academic factors such as age, gender and ethnicity showed varying results. All these factors have implications for nursing programs’ practices and students. Research studies need to begin immediately, not only to define and predict factors for NCLEX-RN success within its new Canadian arena, but also to evaluate the outcomes of this exam at baseline and frequent intervals within this population. This will enhance nursing programs’ support of students’ success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".