Nurse practice environment, workload, burnout, job outcomes, and quality of care in psychiatric hospitals: a structural equation model approach
Bibliographic record
Abstract
AIM: To study the relationships between nurse practice environment, workload, burnout, job outcomes and nurse-reported quality of care in psychiatric hospital staff. BACKGROUND: Nurses' practice environments in general hospitals have been extensively investigated. Potential variations across practice settings, for instance in psychiatric hospitals, have been much less studied. DESIGN: A cross-sectional design with a survey. METHOD: A structural equation model previously tested in acute hospitals was evaluated using survey data from a sample of 357 registered nurses, licensed practical nurses, and non-registered caregivers from two psychiatric hospitals in Belgium between December 2010-April 2011. The model included paths between practice environment dimensions and outcome variables, with burnout in a mediating position. A workload measure was also tested as a potential mediator between the practice environment and outcome variables. RESULTS: An improved model, slightly modified from the one validated earlier in samples of acute care nurses, was confirmed. This model explained 50% and 38% of the variance in job outcomes and nurse-reported quality of care respectively. In addition, workload was found to play a mediating role in accounting for job outcomes and significantly improved a model that ultimately explained 60% of the variance in these variables. CONCLUSION: In psychiatric hospitals as in general hospitals, nurse-physician relationship and other organizational dimensions such as nursing and hospital management were closely associated with perceptions of workload and with burnout and job satisfaction, turnover intentions, and nurse-reported quality of care. Mechanisms linking key variables and differences across settings in these relationships merit attention by managers and researchers.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".