Nursing contributions to virtual models of care in primary care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Since the onset of the COVID-19 pandemic, virtual care has gained increased attention, particularly in primary care for the ongoing delivery of routine services. Nurses have had an increased presence in virtual care and have contributed meaningfully to the delivery of team-based care in primary care; however, their exact contributions in virtual models of primary care remain unclear. The Nursing Role Effectiveness Model, applied in a virtual care and primary care context, outlines the association between structural variables, nursing roles and patient outcomes. The aim of this scoping review is to identify and synthesise the international literature surrounding nurse contributions to virtual models of primary care. METHODS AND ANALYSIS: The Joanna Briggs Institute scoping review methodology will guide this review. We performed preliminary searches in April 2022 and will use CINAHL, MEDLINE, Embase and APA PsycInfo for the collection of sources for this review. We will also consider grey literature, such as dissertations/theses and organisational reports, for inclusion. Studies will include nurses across all designations (ie, nurse practitioners, registered nurses, practical nurses). To ensure studies capture roles, nurses should be actively involved in healthcare delivery. Sources require a virtual care and primary care context; studies involving the use of digital technology without patient-provider interaction will be excluded. Following a pilot test, trained reviewers will independently screen titles/abstracts for inclusion and extract relevant data. Data will be organised using the Nursing Role Effectiveness Model, outlining the virtual care and primary care context (structure component) and the nursing role concept (process component). ETHICS AND DISSEMINATION: This review will involve the collection and analysis of secondary sources that have been published and/or are publicly available. Therefore, ethics approval is not required. Scoping review findings will be published in a peer-reviewed journal and presented at relevant conferences, targeting international primary care stakeholders.
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 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 | 0.106 | 0.100 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.013 |
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".