Situational and Dispositional Influences on Nurses' Workplace Well-being
Bibliographic record
Abstract
BACKGROUND: Unit-level leadership and structural empowerment play key roles in creating healthy work environments, yet few researchers have examined these contextual effects on nurses' well-being. OBJECTIVES: The aim of this study was to test a multilevel model of structural empowerment examining the effect of nursing unit leadership quality and structural empowerment on nurses' experiences of burnout and job satisfaction and to examine the effect of a personal dispositional variable, core self-evaluation, on these nurse experiences. METHODS: Nurses (n = 3,156) from 217 hospital units returned surveys that included measures of leader-member exchange, structural empowerment, burnout, core self-evaluation, and job satisfaction. Multilevel structural equation modeling was used to test the model. RESULTS: Nurses' shared perceptions of leader-member exchange quality on their units positively influenced their shared perceptions of unit structural empowerment (Level 2), which resulted in significantly higher levels of individual nurse job satisfaction (Level 1). Unit-level leader-member exchange quality also directly influenced individual nurse job satisfaction. Unit leader-member exchange quality and structural empowerment influenced emotional exhaustion and cynicism differentially. Higher unit-level leader-member exchange quality was associated with lower cynicism; higher unit-level structural empowerment was associated with lower emotional exhaustion. At Level 1, higher core self-evaluation was associated with lower levels of both emotional exhaustion and cynicism, both of which were associated with lower job satisfaction. DISCUSSION: This study provides a theoretical understanding of how unit leadership affects both unit- and individual-level outcomes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".