Comparing an All-RN Unit to a Mixed-Skill Unit at a Hospital
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the differences in patient complications as well as patient and staff satisfaction between a mixed-skill unit and an all-registered nurse (RN) unit. BACKGROUND: It is recognized that nursing care delivered by RNs results in better outcomes; however, more evidence is needed to support a change to an all-RN unit. METHODS: A mixed unit with RNs and unlicensed assistive personnel was compared with an all-RN unit. Each unit had similar resources. Patient complications and patient and staff satisfaction were measured. Patient complications were reported in terms of 1,000 patient days over the study period to minimize noise fluctuations; t test and χ compared means and frequencies, respectively. RESULTS: The all-RN unit had a lower prevalence of patient complications. Patients reported better pain management, and nurse explanation, and reported higher satisfaction on the all-RN unit. CONCLUSIONS: An all-RN unit provided superior outcomes compared with a mixed-skill unit without additional costs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".