Diffusing Innovative Roles Within Ontario Hospitals: Implementing the Nurse Practitioner as the Most Responsible Provider
Bibliographic record
Abstract
Hospitals require identification of the most responsible provider (MRP) for care of admitted patients. Traditionally, the MRP has been a physician. However, legislation changes within Ontario authorize the nurse practitioner (NP) to admit and provide care for hospital in-patients. There is little evidence illustrating adoption of the NP-as-MRP model in Ontario. Reasons for a delayed adoption of this innovative model of care are unclear and warrant investigation. One hospital implemented the NP-as-MRP as an appropriate and beneficial model to maximize access to care for senior patients. Rogers' (2003) model of diffusion of innovation provides a framework to describe the processes undertaken that led to their adoption of the NP-as-MRP model. Detailed processes are highlighted for hospital leaders, hospital board members, and NPs. Other sites are encouraged to evaluate whether the NP-as-MRP model will be appropriate for specific populations; each site should undertake a process that is as detailed to ensure thorough preparation of all who will be affected by this change. Evaluation of the outcomes of the NP-as-MRP model is necessary and must be population- and institution-specific, as these provide new evidence in the early stages of this recent NP-related innovation in Ontario.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".