Collaboration among Registered Nurses and Licensed Practical Nurses: A Scoping Review of Practice Guidelines
Bibliographic record
Abstract
Professional associations, nurse scholars, and practicing nurses suggest that intraprofessional collaboration between nurses is essential for the provision of quality patient care. However, there is a paucity of evidence describing collaboration among nurses, including the outcomes of collaboration to support these claims. The aim of this scoping review was to examine nursing practice guidelines that inform the registered nurse (RN) and registered/licensed practical nurse (R/LPN) collaborative practice in acute care, summarize and disseminate the findings, and identify gaps in the literature. Ten practice guidelines, all published in Canada, were included in the final scoping review. The findings indicate that many of the guidelines were not evidence informed, which was a major gap. Although the guidelines discussed the structures needed to support intraprofessional collaboration, and most of the guidelines mention that quality patient care is the desired outcome of intraprofessional collaboration, outcome indicators for measuring successful collaborative practice were missing in many of the guidelines. Conflict resolution is an important process component of collaborative practice; yet, it was only mentioned in a few of the guidelines. Future guidelines should be evidence informed and provide outcome indicators in order to measure if the collaborative practice is occurring in the practice setting.
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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 | 0.052 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".