Nurse Practitioner and Physician Collaboration in the Long-Term Care Setting: Secondary Analysis of a Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Over the past decade, the role of nurse practitioners (NPs) in long-term care home (LTCH) settings has significantly expanded. Despite this trend, gaps have been identified in the description of collaborative practices between NPs and physicians in the LTCH sector. This study aimed to characterize the elements of collaboration between NPs and physicians in LTCH settings by applying the "Structured Collaborative Practice Core Model." DESIGN: A secondary analysis of a scoping review that focuses on literature where NPs and physicians collaboratively provided care in LTCH settings. METHODS: The initial scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines and included 60 peer-reviewed articles. Data relevant to the 7 core elements of the Structured Collaborative Practice Core Model-responsibility and accountability, coordination, communication, cooperation, assertiveness, autonomy, and mutual trust and respect-were extracted and analyzed. We included articles that described at least 1 element in the analysis. RESULTS: Twenty-nine articles were included in the secondary analysis. The analysis revealed that coordination (n = 25) and communication (n = 23) were the most frequently reported elements. Coordination was often highlighted through descriptions of care delivery organization and decision-making processes, where NPs provided continuous oversight and referred complex cases to physicians. Effective communication pathways, such as joint rounding and face-to-face meetings, were essential for successful collaboration. In contrast, assertiveness (n = 3) was the least frequently discussed element. CONCLUSION AND IMPLICATIONS: Applying the Structured Collaborative Practice Core Model to the existing literature on NP and physician collaboration in LTCH settings underscores the importance of effective coordination and communication. Future work needs to investigate the historical and hierarchical dynamics influencing the relationship. Understanding these elements will inform strategies to optimize collaborative efforts, ultimately improving patient care outcomes in LTCH settings. The unique dynamics of NP and physician care models need to be considered.
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 | 0.036 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".