The Applicability of the NCLEX-RN to the Canadian Testing Population: A Review of Regulatory Body Evidence
Bibliographic record
Abstract
The NCLEX-RN® was adopted by Canadian regulators in 2011 as the test which entry-level nurses must pass in order to be certified to practice. As part of their justification for adopting the exam, the Canadian regulators pointed to two studies conducted by the National Council of State Boards of Nursing (NCSBN). These studies aimed to determine the applicability of the NCLEX-RN® test plan to the Canadian testing population (NCSBN, 2014),with the NCLEX-RN® providing "a fair, valid, and psychometrically sound measurement" of nursing competencies of entry-level RNs in Ontario, Canada (NCSBN, 2012 , p. 8). The purpose of this article is to report the findings from a review of the above two NCSBN studies in order to assess whether they provide sufficient evidence to conclude that the NCLEX-RN® is applicable to the Canadian testing population. While some evidence was found of the use of best practice principles in survey and research design, both authors call into question the evidence provided by the NCSBN, and deny the claims that the NCLEX-RN®, as currently designed, is an appropriate assessment tool for Canadian entry-level nurses.
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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".