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Record W2170965460 · doi:10.12927/cjnl.2010.21596

A Model for Nurse Practitioner Regulation: Principles Underpinning a Three-Registration Category Approach

2010· article· en· W2170965460 on OpenAlexaffvenueabout
J Wearing, Joyce Black, Karen Kline

Bibliographic record

VenueNursing leadership · 2010
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsCollege & Association of Registered Nurses of Alberta
Fundersnot available
KeywordsFlexibility (engineering)Consistency (knowledge bases)Scope of practiceNursingWork (physics)UnderpinningHealth careScope (computer science)Nursing shortageProcess (computing)Nurse educationPublic relationsMedicinePsychologyPolitical scienceComputer scienceManagement

Abstract

fetched live from OpenAlex

The issue of professional regulation of advanced nursing practice is one of the most controversial in the area of nursing regulation today. The controversy affects the roles and scope of advanced practice as well as education requirements. Nurse practitioner (NP) practice is one kind of advanced nursing practice receiving attention currently, with a view to developing consistent educational and regulatory approaches. Consistency is needed to enhance the mobility and flexibility of these important healthcare provider resources in time of shortages. This paper describes how the regulatory body in British Columbia decided to register only three categories of NP: family/all ages, adult and pediatrics. It describes the state of NP regulation when the work began, the consultation process used in coming to this decision and the principles underlying the decision. Regulators, educators and administrators may benefit from understanding the issues and reasoning presented. Literature to guide this work was lacking, a gap this paper addresses to help inform decision-making in other settings and contexts. Dialogue about this approach may facilitate movement towards consistent approaches to the regulation, education and deployment of NPs in Canada and elsewhere.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.444
GPT teacher head0.438
Teacher spread0.006 · 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 designSimulation or modeling
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

Citations9
Published2010
Admission routes3
Has abstractyes

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