The Acute Care Nurse Practitioner in Ontario: A Workforce Study
Bibliographic record
Abstract
In spite of the long history of nurse practitioner practice in primary healthcare, less is known about nurse practitioners in hospital-based environments because until very recently, they have not been included in the extended class registration (nurse practitioner equivalent) with the College of Nurses of Ontario. Recent changes in the regulation of nurse practitioners in Ontario to include adult, paediatric and anaesthesia, indicates that a workforce review of practice profiles is needed to fully understand the depth and breadth of the role within hospital settings. Here, we present information obtained through a descriptive, self-reported survey of all nurse practitioners working in acute care settings who are not currently regulated in the extended class in Ontario. Results suggest wide acceptance of the role is concentrated around academic teaching hospitals. Continued barriers exist related to legislation and regulation as well as understanding and support for the multiple aspects of this role beyond clinical practice. This information may be used by nurse practitioners, nursing leaders and other administrators to position the role in hospital settings for greater impact on patient care. As well, understanding the need for regulatory and legislative changes to support the hospital-based Nurse Practitioner role will enable greater impact on health human resources and healthcare transformation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".