Examining the role of transformational leadership and mission valence on burnout among hospital staff
Bibliographic record
Abstract
Purpose The present study contributes to our understanding of how to curb burnout among hospital staff over time. The authors extend existing research by examining the mediating role of mission valence in the link between transformational leadership and burnout. Design/methodology/approach Self-administered questionnaire data from employees in a Canadian general hospital ( N = 185) were analyzed using a time-lagged research design to examine whether transformational leaders can increase employees' attraction to the organization's mission (i.e. mission valence) and in turn alleviate long-term burnout. Findings Structural equation modeling analysis demonstrated that transformational leadership (time 1) was negatively related to the burnout components of emotional exhaustion and depersonalization (time 2). Further, the results showed that mission valence mediated these relationships. Practical implications The study findings are important for managers and professionals as they identify transformational leadership as a potent strategy to alleviate employee burnout and clarify the process through which this is achieved, namely, by increasing mission valence. Originality/value To date, surprisingly little research has explored how transformational leadership influences followers' burnout. To address this issue, the present study examined the role of transformational leadership on staff burnout through the mechanism of increasing mission valence. Understanding how to mitigate burnout is particularly critical in health care organizations given that burnout not only negatively impacts employee wellbeing but also the wellbeing and quality of care provided to patients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".