Mapping the provision of care by nurse practitioners in virtual health care clinics: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review will be to chart the evidence in relation to the provision of care by nurse practitioners in virtual health care clinics. INTRODUCTION: The COVID-19 pandemic prompted health care systems, providers, and patients to rapidly shift to virtual care settings. Four years later, virtual care continues to be a focal point of health care delivery with the ensuing spread of both hybrid models (ie, a blend of in-person and virtual care) and virtual clinics (ie, virtual-only service delivery platforms with built-in electronic medical records). Nurse practitioners are identified globally as essential components of the effective and sustainable delivery of health care models. However, while both virtual care and nurse practitioners are recognized as critical cornerstones of health care innovation, there is a gap in what is known about care provision by nurse practitioners in virtual clinics. ELIGIBILITY CRITERIA: Articles exploring care provided by nurse practitioners in virtual health care clinics will be included. Eligible articles will focus on characteristics of nurse practitioners and their provision of virtual care, as well as the barriers and facilitators of care provision. Clinic settings will include those that offer longitudinal and/or episodic care. METHODS: This review will follow the JBI methodology for scoping reviews. The search strategy will aim to locate published and unpublished studies, with no date restrictions. Databases to be searched will include MEDLINE (Ovid), CINAHL (EBSCOhost), Embase (Ovid), Scopus, and PsycINFO (EBSCOhost), as well as ProQuest Dissertations and Theses Global for gray literature. Data will be extracted and organized using a tool informed by the PEPPA conceptual framework and reported in narrative format, accompanied by a tabular summary. REVIEW REGISTRATION: OSF https://osf.io/uf6qg.
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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 | 0.117 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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".