The Tension Between Regulation and the Pursuit of Quality in Canadian Nurse Practitioner Education Programs
Bibliographic record
Abstract
Nurses in advanced practice roles have existed in Canada for over 100 years, yet only in the last two decades, have nurse practitioners (NPs) been recognized as advanced practice nurses (APNs). During this time, NP educational programs have increased and transitioned from post-baccalaureate level to graduate level. Legislation and national NP regulatory approval processes have contributed to existing barriers to NP role implementation and full scope of practice. While regulation is mandatory and focused on public safety, an emphasis towards quality has led to the introduction of a national voluntary NP program accreditation process. The purpose of this paper is to initiate a discussion between Canadian NP regulators and educators related to proposed regulatory approaches and accreditation processes that balance public safety while promoting quality and excellence in NP education. Having two separate and costly processes has led to tension during a time of provincial fiscal restraint on university budgets coupled with the COVID-19 pandemic and its impact on nursing education. An integrated pan-Canadian approach of regulation and accreditation may ensure public safety, continuity, and consistency in quality NP education, enhance mobility of the NP workforce, and systematic planning to guide successful future NP role development and practice.
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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 | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.027 | 0.022 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".