Scope of practice of primary care nurses: a protocol for an umbrella review of international literature
Bibliographic record
Abstract
INTRODUCTION: Primary care nurses (PCNs) are the second largest workforce in primary care and play a critical role in facilitating access to coordinated care and reducing health disparities. There is renewed interest in team-based primary care as a solution for health workforce challenges. Some team models enable PCNs (ie, nurse practitioners, registered nurses, licensed/registered practical nurses) to leverage one another's expertise to work to optimal scope; the extent to which this happens depends on multiple context-dependent factors. We will conduct an umbrella review to synthesise and compare international knowledge syntheses focused on scope of practice enactment (ie., roles and activities) of PCNs in primary care. METHODS AND ANALYSIS: We will conduct the umbrella review according to the Joanna Briggs Institute methodology, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) guidelines, and using the Nursing Care Organization Framework as guidance. We will search a wide range of scientific electronic databases and grey literature sources, and consider articles published in English and French by the Organization for Economic Cooperation and Development and designated key partner countries for inclusion, with no publication date limits. Two independent reviewers will screen titles, abstracts and full-text articles, and any disagreements will be resolved through discussion or by a third reviewer. We will use the Risk of Bias Assessment Tool for Systematic Reviews to assess the quality and risk of bias in the included systematic and scoping reviews. ETHICS AND DISSEMINATION: Results will be presented in a PRISMA Scoping Review flow diagram. We will synthesise data from included studies in a detailed literature review table and develop visual aids to communicate the shared and unique roles and activities of PCN scope of practice. We will disseminate the results of the review through peer-reviewed publications and conferences related to this field. Ethics approval is not required.
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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 | 0.213 | 0.225 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.020 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.069 | 0.021 |
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".