Impacts of unit‐level nurse practice environment and burnout on nurse‐reported outcomes: a multilevel modelling approach
Bibliographic record
Abstract
AIM: To investigate impacts of practice environment factors and burnout at the nursing unit level on job outcomes and nurse-assessed quality of care in acute hospital nurses. BACKGROUND: Prior research has consistently demonstrated correlations between nurse practice environments and nurses' job satisfaction and health at work, but somewhat less evidence connects practice environments with patient outcomes. The relationship has also been more extensively documented using hospital-wide measures of environments as opposed to measures at the nursing unit level. DESIGN: Survey. METHOD: Data from a sample of 546 staff nurses from 42 units in four Belgian hospitals were analysed using a two-level (nursing unit and nurse) random intercept model. Linear and generalised linear mixed effects models were fitted including nurse practice environment dimensions measured with the Revised Nursing Work Index and burnout dimensions of the Maslach Burnout Inventory as independent variables and job outcome and nurse-assessed quality of care variables as dependent variables. RESULTS: Significant unit-level associations were found between nurse practice environment and burnout dimensions and job satisfaction, turnover intentions and nurse-reported quality of care. Emotional exhaustion is a predictor of job satisfaction, nurse turnover intentions and assessed quality of care as well besides various nurse work practice environment dimensions. Nurses 'ratings of unit-level management and hospital-level management and organisational support had effects in opposite directions on assessments of quality of care at the unit; this suggests that nurses' perceptions of conditions on their nursing units relative to their perceptions of their institutions at large are potentially influential in their overall job experience. CONCLUSION: Nursing unit variation of the nurse practice environment and feelings of burnout predicts job outcome and nurse-reported quality of care variables. RELEVANCE TO CLINICAL PRACTICE: The team and environmental contexts of nursing practice play critical roles in the recruitment and retention of nurses, and as well as in the quality of care delivered. Widespread burnout as a nursing unit characteristic, reflecting a response to chronic organisational stressors, merits special attention from staff nurses, physicians, managers and leaders.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".