Associations of patient safety outcomes with models of nursing care organization at unit level in hospitals
Bibliographic record
Abstract
OBJECTIVE: To examine the associations of four distinct nursing care organizational models with patient safety outcomes. DESIGN: Cross-sectional correlational study. Using a standardized protocol, patients' records were screened retrospectively to detect occurrences of patient safety-related events. Binary logistic regression was used to assess the associations of those events with four nursing care organizational models. SETTING: Twenty-two medical units in 11 hospitals in Quebec, Canada, were clustered into 4 nursing care organizational models: 2 professional models and 2 functional models. PARTICIPANTS: Two thousand six hundred and ninety-nine were patients hospitalized for at least 48 h on the selected units. MAIN OUTCOME MEASURE: Composite of six safety-related events widely-considered sensitive to nursing care: medication administration errors, falls, pneumonia, urinary tract infection, unjustified restraints and pressure ulcers. Events were ultimately sorted into two categories: events 'without major' consequences for patients and events 'with' consequences. RESULTS: After controlling for patient characteristics, patient risk of experiencing one or more events (of any severity) and of experiencing an event with consequences was significantly lower, by factors of 25-52%, in both professional models than in the functional models. Event rates for both functional models were statistically indistinguishable from each other. CONCLUSIONS: Data suggest that nursing care organizational models characterized by contrasting staffing, work environment and innovation characteristics may be associated with differential risk for hospitalized patients. The two professional models, which draw mainly on registered nurses (RNs) to deliver nursing services and reflect stronger support for nurses' professional practice, were associated with lower risks than are the two functional models.
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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 | 0.005 | 0.031 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".