Americanizing Canadian Nursing: Nursing Regulation Drift
Bibliographic record
Abstract
Recent regulatory changes mean Canadian nurses are writing a US-based entry to practice exam and a US company is assessing credentials of internationally educated nurses (IENs) for Canadian registration.This paper asserts that this policy direction has significant consequences for Canadian content and integrity of education programs, francophone parity in testing, and the future of primary health care and health system reform.Furthermore, writing a US exam means Canada is at risk of losing nursing human resources to the United States while trade agreements endanger Canadian nursing intellectual property.À la suite de changements de réglementation, les diplômés en science infirmière doivent passer un examen d'autorisation à pratiquer mis au point aux EU, et les diplômés internationaux en science infirmière sont évalués par une entreprise, elle aussi des États-Unis.Dans cet article, nous considérons que ces changements ont un impact significatif sur le contenu canadien et l'intégrité des programmes de formation, l'égalité de traitement dans l'évaluation pour les francophones et le futur des soins de santé primaire et la réforme des soins de santé.De plus, en uniformisant l'accréditation avec les EU, le Canada court le risque de laisser partir sa main d'oeuvre infirmière dans ce pays, au moment même où les accords commerciaux mettent en danger la propriété intellectuelle infirmière canadienne.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.036 | 0.015 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".