Registered Nurses and Licensed/Registered Practical Nurses: A Description and Comparison of Their Decision-Making Process
Bibliographic record
Abstract
In many parts of Canada, nursing care is provided by registered nurses (RNs) and licensed/registered practical nurses (L/RPNs). The profession, regulatory bodies and employers are struggling to define their similarities and differences in their attempts to ensure patients are receiving the right care by the right care provider. An understanding of the decision making of nurses presents one way of differentiating between their overlapping roles. Nursing decision-making is a complex cognitive process. Assessment occurs and problems are postulated. Possible alternatives, with their risks and benefits, outcomes and likelihood of outcomes are identified. Preferences and values are considered, and an intervention is selected. The best way to implement an intervention is determined, implementation follows and evaluation takes place. In this research, a triangulated design was used to examine and compare the decision-making process of RNs and L/RPNs. Analysis revealed that nurses consider themselves to be frequently involved in elements that are part of the decision-making process. Nurses attribute the difficulty encountered to the context within which decision making occurs. Differences exist between the RN and L/RPN in the frequency of their involvement with most of the elements of the process. Differences in difficulty encountered with these elements were less pronounced.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| 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".