Development and Evaluation of an RN/RPN Utilization Toolkit
Bibliographic record
Abstract
PURPOSE: To develop and evaluate a toolkit for Registered Nurse/Registered Practical Nurse (RN/RPN) staff mix decision-making based on the College of Nurses of Ontario's practice standard for utilization of RNs and RPNs. METHODS: Descriptive exploratory. The toolkit was tested in a sample of 2,069 inpatients on 36 medical/surgical units in five academic and two community acute care hospitals in southern Ontario. Survey and focus group data were used to evaluate the toolkit's psychometric properties, feasibility of use and utility. RESULTS: Results support the validity and reliability of the Patient Care Needs Assessment (PCNA) tool and the consensus-based process for conducting patient care reviews. Review participants valued the consensus approach. There was limited evidence for the validity and utility of the Unit Environmental Profile (UEP) tool. Nursing unit leaders reported confidence in planning unit staff mix ratios based on information generated through application of the toolkit, specifically the PCNA, although they were less clear about how to incorporate environmental data into staff mix decisions. CONCLUSIONS: Results confirm that the toolkit consistently measured the constructs that it was intended to measure and was useful in informing RN/RPN staff mix decision-making. Further refinement and testing of the UEP is required. Future research is needed to evaluate the quality of decisions resulting from the application of the toolkit, illuminate processes for integrating data into decisions and adapt the toolkit for application in other sectors.
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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 | 0.087 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".