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Record W2802476689 · doi:10.12927/cjnl.2017.25448

Diffusing Innovative Roles Within Ontario Hospitals: Implementing the Nurse Practitioner as the Most Responsible Provider

2017· article· en· W2802476689 on OpenAlexaffvenueabout
Christina Hurlock‐Chorostecki, Michelle Acorn

Bibliographic record

VenueNursing leadership · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsCentre for Global Health ResearchSt Joseph's Health CareWestern University
Fundersnot available
KeywordsNursingWarrantBusinessNurse practitionersLegislationPopulationMedicineHealth carePolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0120.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.349
GPT teacher head0.483
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes3
Has abstractyes

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