The Forgotten: The Challenges Faced by Francophone Nursing Candidates following the Introduction of the NCLEX-RN in Canada
Bibliographic record
Abstract
In 2015, the traditional paper-and-pencil entry-to-practice exam in Canada was replaced by a computer-adaptive testing exam, the American National Council Licensure Examination for Registered Nurses (NCLEX-RN). As there are two official languages in Canada - English and French - the NCLEX-RN was translated to French. Although initially the pass rates for anglophone writers with the NCLEX-RN were lower than with the previous Canadian licensing exam, their pass rates have now increased. By contrast, francophone writers have continued to have lower pass rates, and a decreasing number of candidates are choosing to write the exam in French. A lack of access to French language preparatory resources is being reported by francophone graduates as one of the contributing factors. Canadian nursing regulators report that they are not responsible for ensuring that candidates have access to preparatory materials. However, given the bilingual culture and heritage in Canada, there is a responsibility to ensure equitable access to preparatory resources to ensure success on the licensing exam. This paper raises alarm about the decreasing number of francophone graduates writing the NCLEX-RN in French and the ongoing delivery of safe, quality nursing care to francophone patients by nurses proficient in the French language.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".