Recommendations for NP/Physician Collaborative Relationships in Long-Term Care Homes
Bibliographic record
Abstract
Residents of long-term care (LTC) homes have complex medical and psychosocial care needs. Nurse practitioners (NPs) and physicians possess the complementary skills required for responding to residents' needs. Therefore, collaboration between NPs and physicians is crucial for achieving optimal resident outcomes. Despite the increasing number of NPs and physicians practicing in LTC homes, the recruitment and retention of both primary care providers in the LTC sector is difficult. In addition, establishing collaborative relationships remains challenging. Informed by 2 literature reviews, interviews with NPs and physicians with LTC experience, and a stakeholder workshop, this article provides a set of evidence-based considerations for physicians and NPs for establishing collaborative relationships. The recommendations are divided into 5 areas: (1) creating clear expectations, (2) establishing best practices for communication, (3) fostering a culture of psychological safety, (4) maintaining a healthy relationship, and (5) developing educational supports. Implications for practice, policy, and research include establishing support from LTC home management, resources for managing the increasing complexity of LTC residents, and continuing education courses focused on the LTC environment. The current and anticipated care needs of residents in LTC require a shift to effective collaborative relationships between NPs and physicians. These evidence-based recommendations can enhance collaborative NP/physician relationships and their work environment and assist with the retention of NPs and physicians in LTC homes.
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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 | 0.053 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.032 | 0.024 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".