Hospital nurse practice environment, burnout, job outcomes and quality of care: test of a structural equation model
Bibliographic record
Abstract
AIM: The aim of the study was to investigate relationships between nurse practice environment, burnout, job outcomes and nurse-assessed quality of care. BACKGROUND: A growing line of work confirms that, in countries with distinctly different healthcare systems, nurses report similar shortcomings in their work environments and the quality of care in hospitals. Neither the specific work environment factors most involved in dissatisfaction, burnout and other negative job outcomes, and patient outcomes, nor the mechanisms tying nurse job outcomes to quality of care are well understood. METHOD: A Nurse Practice Environment and Outcome causal structure involving pathways between practice environment dimensions and outcome variables with components of burnout in a mediating position was developed. Survey data from 401 staff nurses across 31 units in two hospitals (including the Revised Nursing Work Index, the Maslach Burnout Inventory, and job outcome and nurse-assessed quality of care variables) were used to test this model using structural equation modelling techniques. The data were collected from December 2006 to January 2007. RESULTS: Goodness of fit statistics confirmed an improved model with burnout dimensions in mediating positions between nurse practice environment dimensions and both job outcomes and nurse-assessed quality of care, explaining 20% and 46% of variation in these two indicators, respectively. CONCLUSION: These findings suggest that hospital organizational properties, including nurse-physician relations, are related to quality of care assessments, and to the outcomes of job satisfaction and turnover intentions, with burnout dimensions appearing to play mediating roles. Additionally, a direct relationship between assessments of care quality and management at the unit level was observed.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".