The impact of transformational leadership on nurse faculty satisfaction and burnout during the <scp>COVID</scp> ‐19 pandemic: A moderated mediated analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».