The impact of transformational leadership on nurse faculty satisfaction and burnout during the <scp>COVID</scp> ‐19 pandemic: A moderated mediated analysis
Bibliographic record
Abstract
AIMS: To examine the effects of nursing deans/directors' transformational leadership behaviours on academic workplace culture, faculty burnout and job satisfaction. BACKGROUND: Transformational leadership is an imperative antecedent to organizational change, and employee well-being and performance. However, little has been espoused regarding the theoretical and empirical mechanisms by which transformational leaders improve the academic workplace culture and faculty retention. DESIGN: A cross-sectional survey design was implemented. METHODS: Nursing faculty employed in Canadian academic settings were invited to complete an anonymous online survey in May-July 2021. A total of 645 useable surveys were included in the analyses. Descriptive statistics and reliability estimates were performed. The moderated mediation model was tested using structural equation modelling in the Analysis of Moment software v24.0. Bootstrap method was used to estimate total, direct and indirect effects. RESULT: The proposed study model was supported. Transformational leadership had both a strong direct effect on workplace culture and job satisfaction and an inverse direct effect on faculty burnout. While workplace culture mediated the effect of leadership on job satisfaction and burnout, the moderation effect of COVID-19 was not captured in the baseline model. CONCLUSION: The findings provide an in-depth understanding of the factors that affect nursing faculty wellness, and evidence that supportive workplace culture can serve as an adaptive mechanism through which transformational leaders can improve retention. A transformational dean/director can proactively shape the nature of the academic work environment to mitigate the risks of burnout and improve satisfaction and ultimately faculty retention even during an unforeseen event, such as a pandemic. IMPLICATION: Given the range of uncertainties associated with COVID-19, administrators should consider practicing transformational leadership behaviours as it is most likely to be effective, especially in times of uncertainty and chaos. In doing so, academic leaders can work towards equitable policies, plans and decisions and rebuild resources to address the immediate and long-term psychological and overall health impacts of COVID-19.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".