Effects of Individual Nurse and Hospital Characteristics on Patient Safety and Quality of Care
Bibliographic record
Abstract
Aim: The aim of the study was to investigate the effects of individual nurse and hospital characteristics and their interactions on patient adverse events and quality of care using a multilevel approach. \nBackground: Because nurses constitute the major workforce in healthcare systems, they are key potential contributors to enhancement of patient safety and quality of care. Therefore, researchers have investigated factors affecting nursing care and have suggested that both individual nurse and organizational characteristics can play a significant role in patient safety and quality of care. However, the relative contribution of these characteristics remains unclear. \nMethods: Based on a multilevel conceptual framework, data for 1,053 Canadian nurses who provided direct care to patients in acute care hospitals were analyzed using multilevel modeling. \nResults: The study revealed that organizational safety culture was significantly associated with three types of patient adverse events as well as quality of care. Controlling for the effects of nurse and hospital characteristics, nurses in hospitals with 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 than nurses in hospitals with weaker safety culture. In addition, nurses who worked in hospitals with a stronger safety culture were significantly more likely to report higher levels of quality of care in their hospitals. Additional analyses showed that the effects of individual-level baccalaureate education and years of experience on quality of care differed across hospitals. Among hospital characteristics, only hospital-level nurse education interacted with \nindividual-level baccalaureate education. No significant interaction was found between individual-level nurse experience and hospital-level variables. \nConclusions: This study makes significant contributions to existing knowledge regarding the positive effect of organizational safety culture on patient adverse events and quality of care. With the current healthcare emphasis on continuous quality improvement, healthcare organizations should strive to improve their safety culture by creating environments where healthcare providers can trust each other, work collaboratively, and share accountability for patient safety and quality of care.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".