Developing effective strategies to strengthen collaborative practice between registered nurses and registered practical nurses through action research
Bibliographic record
Abstract
Collaborative practice in health care is essential as it strengthens the relationship among teams and enhances an integrative work platform. Registered Nurses (RNs) and Registered Practical Nurses (RPNs) work together across different care settings in a supportive role that enhances patient outcomes, job satisfaction, and retention. Nursing stands as the largest healthcare profession in the nation, boasting nearly 4.2 million nurse's nationwide (Statistics Canada, 2022). In Canada, nurses constitute the largest segment of regulated health professionals, comprising approximately half of the total health workforce. However, globally, there has been a shortage of nurses attributed to burnout, physical injuries, and job dissatisfaction (Statistics Canada, 2022). To address the nursing shortage, several healthcare services have adopted the nursing skilled-mix model, facilitating collaboration between RNs and RPNs. At NHU, over the last six years, this practice model has been utilized to adequately staff nurses on Specialized Acute Care (SAC) units. Nonetheless, no formal study has been conducted to explore the experience of RNs and RPNs regarding collaborative practice. Qualitative action research was conducted to comprehend challenges and pinpoint effective strategies for strengthening collaborative practice among RNs and RPNs. Cycle 1 findings from participating RNs and RPNs revealed the necessity for broader discussions involving nursing leadership staff in Cycle 2. There were clear indications of knowledge gaps regarding scope of practice, disparities in assignments and professional development opportunities for RPNs resulted in ethical dilemmas, power imbalance between RNs, and RPNs regarding autonomy and the need for organizational leadership to take accountability to devise effective collaborative strategies were some of the major barriers to collaborative practice.--Author's abstract
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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 | 0.188 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".