Nurse Staffing Models, Nursing Hours, and Patient Safety Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND DATA: Limited research has been conducted examining the effect of nurse staffing models on costs and patient outcomes. OBJECTIVE: The objective of this study was to evaluate the effect of different nurse staffing models on costs and the patient outcomes of patient falls, medication errors, wound infections, and urinary tract infections. METHODS: A descriptive correlational study was conducted in all of the 19 teaching hospitals in Ontario, Canada. The sample comprised hospitals and adult medical, surgical, and obstetric inpatients within those hospitals. RESULTS: The lower the proportion of professional nursing staff employed on a unit, the higher the number of medication errors and wound infections. The less experienced the nurse, the higher the number of wound infections. Nurse staffing models that included a lower proportion of professional nursing staff in the mix used more nursing hours in this study. CONCLUSIONS: The results of this study suggest that a higher proportion of professional nurses in the staff mix (RNs/RPNs) on medical and surgical units in Ontario teaching hospitals are associated with lower rates of medication errors and wound infections. Higher patient complexity was associated with greater patient use of nursing care resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it