What Do We Know About Nurse Practitioner/Physician Care Models in Long-Term Care: Results of a Scoping Review
Bibliographic record
Abstract
OBJECTIVES: Due to the rise of the nurse practitioner (NP) role in long-term care settings, it is important to understand the underlying structures and processes that influence NP and physician care models. This scoping review aims to answer the question, "What are the structures, processes, and outcomes of care models involving NPs and physicians in long-term care (LTC) homes?" A secondary aim was to describe the structural enablers and barriers across care models. RESEARCH DESIGN AND METHODS: Seven databases were searched. Studies that described NPs and physicians working in LTC were identified and included in the review. We stratified the findings by care model and synthesized using the Donabedian model, which evaluates health care quality based on 3 dimensions: structure, process, and outcome. We then categorized macro, meso, and micro structural enablers and barriers. RESULTS: Sixty papers were included in the review. The main structural influencers within 5 care models included policies on scope of practice, clarity of role description, and workload. A limited number of papers referred to the process of enabling the development of a working relationship. Thirty-five (49%) studies described resident, staff, and health system outcomes. CONCLUSIONS AND IMPLICATIONS: Although structural characteristics of NP and physician care models are described in-depth, there is less detail on the processes that occur within the NP and physician care models. We highlight structural barriers and enablers within the care models, allowing for recognition of the importance of organizational influence on the NP and physician relationship. Future work should focus on the processes of the relationships in the models by identifying the drivers and initiators of collaboration between NPs and physicians and how these relationships influence outcomes.
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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 | 0.038 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".