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Record W6887728272 · doi:10.17605/osf.io/u6ehk

Strengthening Team-Based Care: Optimizing the Scope of Practice of Primary Care Nurses-Protocol

2025· other· en· W6887728272 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsScope of practiceScope (computer science)WorkforcePrimary careHealth careWork (physics)Population healthLeverage (statistics)

Abstract

fetched live from OpenAlex

Goal: To strengthen team-based primary care and primary care nurses' (PC-Ns: Nurse Practitioners [NPs], Registered Nurses [RNs], Licensed/Registered Practical Nurses [LPN/RPNs]) intra-professional collaboration by delineating professional scopes of practice of each PC-N provider. Background and Rationale: In Canada, three regulated nursing designations work in primary care (NPs,RNs, LPNs/RPNs). PC-Ns are the second largest workforce in primary care and play critical roles in optimizing outcomes, while reducing health disparities. There is renewed interest in team-based primary care that includes nurses as a solution for health workforce challenges. Some team models enable PC-Ns to leverage one another’s expertise to work to optimal scope, while other models do not. A recent Canadian Institute for Health Information report compared PC-Ns’ legislated scopes of practice but did not offer insights into PC-Ns’ professional scopes to meet patient and population health needs. We will examine PCNs’ professional scopes of practice and how the presence of other PC-Ns and providers impacts scope enactment. Research Questions and Objectives: How do PC-N professional scopes of practice overlap/differ? How does the presence/absence of other PC-Ns impact (narrow, expand, and/or shift) activities of each PC-N provider? How does the presence/absence of other non-PC-N providers impact activities of each PC-N provider? How do characteristics of the clinical setting influence PC-Ns’ professional scope of practice enactment? This project consists of two linked studies: 1) umbrella review and 2) multiple case study. The objectives are: 1. To synthesize and compare international knowledge syntheses focused on scope of practice enactment (i.e., roles/activities) of PC-Ns in primary care. 2. To examine how nursing care organization (i.e., staffing, practice attributes) impacts professional scope of practice enactment of PC-N providers in a primary care team. Methods: The Umbrella Review will consolidate international scoping and systematic review evidence on PC-N roles into a single report. The Multiple Case Study consists of two compoenents: (1) qualitative interviews and (2) a measure of PC-N scope enactment (Actual Scope of Practice-Primary Care tool) in four provinces (Alberta, Ontario, Quebec, Newfoundland & Labrador). PC-Ns will describe their scope of practice, challenges/facilitators, and opportunities for role optimization. Overlap in care will be examined by presenting PC-Ns with clinical scenarios and prompting discussion about care activities. Thematic analysis will be used. Data will be compared across cases. Expertise: We are an interdisciplinary team comprising established and early-career researchers, persons with lived experience, and decision-makers. Our expertise spans PC-N, health workforce, practice regulation, and knowledge translation. Strong collaborations with knowledge users and primary care researchers enhance the project's robustness and allow for broad perspectives. Outcomes and Significance: This project will generate evidence about the extent to which PC-N scopes are optimized/enacted in teams and guide funding and staffing policies to ensure that the best qualified nurse, or team of nurses, is in place to realize the benefits of team-based care. Findings will enhance understanding of PC-Ns’ shared/unique contributions to patient care, strengthen collaboration, and enhance the capacity of primary care to optimize team configurations.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0110.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.370
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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