The impact of nursing work environments on patient safety outcomes: the mediating role of burnout/engagement
Bibliographic record
Abstract
OBJECTIVE: To test a theoretical model of professional nurse work environments linking conditions for professional nursing practice to burnout and, subsequently, patient safety outcomes. BACKGROUND: The 2004 Institute of Medicine report raised serious concerns about the impact of hospital restructuring on nursing work environments and patient safety outcomes. Few studies have used a theoretical framework to study the nature of the relationships between nursing work environments and patient safety outcomes. METHODS: Hospital-based nurses in Canada (N = 8,597) completed measures of worklife (Practice Environment Scale of the Nursing Work Index), burnout (Maslach Burnout Inventory-Human Service Scale), and their report of frequency of adverse patient events. RESULTS: Structural equation modeling analysis supported an extension of Leiter and Laschinger's Nursing Worklife Model. Nursing leadership played a fundamental role in the quality of worklife regarding policy involvement, staffing levels, support for a nursing model of care (vs medical), and nurse/physician relationships. Staffing adequacy directly affected emotional exhaustion, and use of a nursing model of care had a direct effect on nurses' personal accomplishment. Both directly affected patient safety outcomes. CONCLUSIONS: The results suggest that patient safety outcomes are related to the quality of the nursing practice work environment and nursing leadership's role in changing the work environment to decrease nurse burnout.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".