Work engagement supports nurse workforce stability and quality of care: nursing team‐level analysis in psychiatric hospitals
Bibliographic record
Abstract
Accessible summary Burnout and work engagement are two sides of one coin, two opposite poles related not only to how workers personally experience their jobs but also to how they experience their jobs within the context of work teams/groups. Engaged workers have a lot of energy, are very enthusiastic about their jobs and are absorbed by their work. Nurses’ job performance in hospitals, including psychiatric hospitals, is dependent upon their relationships with physicians and other healthcare workers and their superiors, how they are involved in the decisions about their work and whether or not they are provided with the right resources and adequate support. When nursing teams are able to perform well, nurses tend to be more engaged and satisfied with their jobs and are more willing to stay in their positions. Engaged nursing teams report better quality of patient care in psychiatric hospitals. Abstract Research in healthcare settings reveals important links between work environment factors, burnout and organizational outcomes. Recently, research focuses on work engagement, the opposite (positive) pole from burnout. The current study investigated the relationship of nurse practice environment aspects and work engagement (vigour, dedication and absorption) to job outcomes and nurse‐reported quality of care variables within teams using a multilevel design in psychiatric inpatient settings. Validated survey instruments were used in a cross‐sectional design. Team‐level analyses were performed with staff members ( n = 357) from 32 clinical units in two psychiatric hospitals in B elgium. Favourable nurse practice environment aspects were associated with work engagement dimensions, and in turn work engagement was associated with job satisfaction, intention to stay in the profession and favourable nurse‐reported quality of care variables. The strongest multivariate models suggested that dedication predicted positive job outcomes whereas nurse management predicted perceptions of quality of care. In addition, reports of quality of care by the interdisciplinary team were predicted by dedication, absorption, nurse–physician relations and nurse management. The study findings suggest that differences in vigour, dedication and absorption across teams associated with practice environment characteristics impact nurse job satisfaction, intention to stay and perceptions of quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".