Perspectives of Nurse Practitioner–Physician Collaboration among Nurse Practitioners in Canadian Long-Term Care Homes: A National Survey
Bibliographic record
Abstract
Nurse practitioners (NPs) can play an important role in providing primary care to residents in long-term care (LTC) homes. However, relatively little is known about the day-to-day collaboration between NPs and physicians (MDs) in LTC, or factors that may influence this collaboration. Survey data from NPs in Canadian LTC homes were used to explore these issues. Thirty-seven of the 45 (82%) identified LTC NPs across Canada completed the survey. NPs worked with an average of 3.4 MDs, ranging from 1-26 MDs. The most common reasons for collaborating included managing acute and chronic conditions, and updating MDs on resident status changes. Satisfaction with NP-MD collaboration was high, and did not significantly differ among NPs working full versus part time, NPs working in a single versus multiple homes, or NPs with more versus less experience. By understanding the nature of NP-MD collaboration, we can identify ways of supporting and enhancing collaboration between these professionals.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".