Nurse Staffing Models as Predictors of Patient Outcomes
Bibliographic record
Abstract
BACKGROUND: Little research has been conducted that examined the intended effects of nursing care on clinical outcomes. OBJECTIVE: The objective of this study was to evaluate the impact of different nurse staffing models on the patient outcomes of functional status, pain control, and patient satisfaction with nursing care. RESEARCH DESIGN: A repeated-measures study was conducted in all 19 teaching hospitals in Ontario, Canada. SUBJECTS: The sample comprised hospitals and adult medical-surgical and obstetric inpatients within those hospitals. MEASURES: The patient's functional health outcomes were assessed with the Functional Independence Measure (FIM) and the Medical Outcome Study SF-36. Pain was assessed with the Brief Pain Inventory and patient perceptions of nursing care were measured with the nursing care quality subscale of the Patient Judgment of Hospital Quality Questionnaire. RESULTS: The proportion of regulated nursing staff on the unit was associated with better FIM scores and better social function scores at hospital discharge. In addition, a mix of staff that included RNs and unregulated workers was associated with better pain outcomes at discharge than a mix that involved RNs/RPNs and unregulated workers. Finally, patients were more satisfied with their obstetric nursing care on units where there was a higher proportion of regulated staff. CONCLUSIONS: The results of this study suggest that a higher proportion of RNs/RPNs on inpatient units in Ontario teaching hospitals is associated with better clinical outcomes at the time of hospital discharge.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".