Nurse practitioner/physician collaborative models of care: a scoping review protocol
Bibliographic record
Abstract
Abstract Background Before the COVID-19 pandemic, many long-term care (LTC) homes experienced difficulties in providing residents with access to primary care, typically delivered by community-based family physicians or nurse practitioners (NPs). During the pandemic, legislative changes in Ontario, Canada enabled NPs to act in the role of Medical Directors thereby empowering NPs to work to their full scope of practice. Emerging from this new context, it remains unclear how NPs and physicians will best work together as primary care providers. NP/physician collaborative models appear key to achieving optimal resident outcomes. This scoping review aims to map available evidence on existing collaborative models of care between NPs and physicians within LTC homes. Methods The review will be guided by the research question, “What are the structures, processes and outcomes of collaborative models of care involving NPs and Physicians in LTC homes?” This scoping review will be conducted according to the methods framework for scoping reviews outlined by Arksey and O’Malley and refined by Levac et al., Colquhoun et al., and Daudt et al., as well as the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Statement. Electronic databases (MEDLINE, Embase + Embase Classic, APA PsycInfo, Cochrane Central Register of Controlled Trials, AMED, CINAHL, Ageline, and Scopus), grey literature, and reference lists of included articles will be searched. English language studies that describe NP and physician collaborative models within the LTC setting will be included. Discussion This scoping review will consolidate what is known about existing NP/physician collaborative models of care in LTC homes. Results will be used to inform the development of a collaborative practice framework for long-term care clinical leadership.
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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 | 0.131 | 0.109 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.018 |
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".