The Educational Terrain of Preparing Registered Nurses to Prescribe: An Environmental Scan
Bibliographic record
Abstract
Expanded nursing roles are being explored in Canada as a means to better support the health of the population, enable access to quality care and contribute to the sustainability of the healthcare system. As Canada embarks on a process of developing and implementing registered nurse (RN) prescribing roles, gathering evidence from jurisdictions with established nurse prescribing is helpful to inform policy development. Of particular interest is literature from the UK, with more than 20 years of experience with nurse prescribing, which identifies the importance of completing graduate pharmacological education and building on existing clinical knowledge and experience. Similar models of RN prescribing education have been adopted in New Zealand and Ireland. Within Canada, the RN prescribing role is still in its infancy, and there is some variation among provinces in the approach to prescribing practices and in RN prescribing education. This paper describes the results of an environmental scan that sought to explore the educational practices of national and international jurisdictions through published and grey literature sources. Findings from this environmental scan will support nurse leaders as they develop RN prescribing regulation and education in Canada and will highlight important areas for further knowledge development.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".