Effects of Individual Nurse and Hospital Characteristics on Patient Adverse Events and Quality of Care: A Multilevel Analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate effects of individual nurse and hospital characteristics on patient adverse events and quality of care using a multilevel approach. DESIGN: This is a secondary analysis of a combination of nurse survey data (N = 1,053 nurses) and facility data (N = 63 hospitals) in Canada. METHODS: Multilevel ordinal logistic regression was employed to examine effects of individual nurse and hospital characteristics on patient adverse events. Multilevel linear regressions were used to investigate effects of individual nurse and hospital characteristics on quality of care. FINDINGS: Organizational safety culture was associated with patient adverse events and quality of care. Controlling for effects of nurse and hospital characteristics, nurses in hospitals with a stronger safety culture were 64% less likely to report administration of wrong medication, time, or dose; 58% less likely to report patient falls with injury; and 60% less likely to report urinary tract infections; and were more likely to report higher levels of quality of care. Additionally, the effects of individual-level baccalaureate education and years of experience on quality of care differed across hospitals, and hospital-level nurse education interacted with individual-level baccalaureate education. CONCLUSIONS: This study makes significant contributions to existing knowledge regarding the positive effect of organizational safety culture on patient adverse events and quality of care. CLINICAL RELEVANCE: Healthcare organizations should strive to improve their safety culture by creating environments where healthcare providers trust each other, work collaboratively, and share accountability for patient safety and care quality.
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How this classification was reachedexpand
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".