Staff engagement as a target for managing work environments in psychiatric hospitals: implications for workforce stability and quality of care
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine relationships between practice environment ratings, workload, work engagement, job outcomes and assessments of quality of care in nursing personnel in psychiatric hospitals. DESIGN: Cross-sectional survey. BACKGROUND: A broad base of research studies in health care reveals important links between work environment factors, staff burnout and organisational outcomes that merit examination in inpatient mental healthcare settings. Work engagement, a positively framed parallel construct for burnout, may offer an additional insight into the impacts of work on staff. METHODS: A sample of 357 registered nurses (65·5%), licensed practical nurses (23·5%) and non-registered caregiver (10·6%) of two Belgian psychiatric hospitals were surveyed. A causal model was tested using structural equation modelling, whereby it was proposed that work engagement would be influenced by work environment factors and itself impact perceived quality of care and staff job outcomes such as job satisfaction and turnover intentions. RESULTS: An adjusted model was confirmed. Practice environment features influenced staff vigour and dedication and demonstrated positive effects on job satisfaction, turnover intentions and perceived quality of care through their effects on absorption. CONCLUSION: The findings of this study suggest that work engagement is a likely direct consequence of practice environments that may ultimately have impacts on both staff and patient outcomes. RELEVANCE TO CLINICAL PRACTICE: Leaders, nurse managers, clinicians as well as nurses themselves should be aware of the importance of work environments in mental healthcare facilities that favour engagement. Future efforts should focus on developing and sustaining practice environments that engage mental healthcare workers within interdisciplinary teams with the goal of creating a stable workforce possessing optimal possible knowledge, skills and abilities for delivering 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".