A mixed-methods pilot study of the factors that influence collaboration among registered nurses and registered practical nurses in acute care
Bibliographic record
Abstract
Objective: Staffing models employing registered nurses (RNs) and registered practical nurses (RPN) have created the opportunity for enhanced collaboration in acute care settings. However, little is understood about how these nurses collaborate and the factors that influence their collaboration. The purpose of this pilot study was to examine the factors that influenced collaboration among RNs and RPNs at one acute care hospital in Canada in order to understand and improve nursing collaborative practice.Methods: Using an explanatory, sequential mixed methods design, data were collected over several months in 2016 from the nurses using a questionnaire and individual telephone interviews. Sixty-five RNs and RPNs working on medical, surgical and emergency units completed the “Nurse-Nurse Collaboration Scale” survey and ten RNs and RPNs participated in the telephone interviews.Results: Quantitative analysis showed lower scores among younger nurses across most domains of the survey: conflict management, communication, shared processes, coordination and professionalism. Qualitative analysis revealed working to full scope of practice was a facilitator of RN-RPN collaboration, and older age and poor interpersonal skills were barriers to successful collaboration.Conclusions: The results provide discussion for identification of strategies to improve collaborative practice among nurses such as establishing joint education programs for RNs and RPNs, and the use of models or frameworks to guide collaborative practice in healthcare organizations.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 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".