A Closer Look at the Introduction of the NCLEX-RN in Canada
Bibliographic record
Abstract
In 2015, the nursing faculty across most of Canada were informed by provincial nursing regulators that the America-based National Council Licensure Examination-Registered Nurse (NCLEX-RN) was going to replace the Canadian Registered Nurse Examination for our nursing students to become registered as nurses. This change in the exam has presented a number of challenges to both faculty and students as they transitioned from a Canadian exam for the Canadian context to an exam that was originally formulated for nursing registration in the United States. This manuscript examines the differences in the Canadian and American healthcare systems; explores the variations in Canadian and American nursing practice and education; outlines concerns surrounding the use of standardized testing that panders to corporate interests, encourages "teaching to the test" and is costing nursing programs and nursing students considerable resources; and explores the controversy surrounding the validity of the NCLEX-RN in both Canada and the United States. This manuscript asks Canadian nursing leaders, educators, clinicians and researchers to question why Canadians have taken on this exam when Americans themselves state that this exam "gives a false and incomplete picture of practice readiness."
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".