BURNOUT IN HEALTHCARE AFTER THE PANDEMIC: PSYCHOEDUCATION AND DESTIGMATISATION AS KEYS TO PREVENTION
Notice bibliographique
Résumé
The COVID-19 pandemic has raised the psychological stress experienced by healthcare workers worldwide to historic levels. Nurses, doctors and therapists were confronted not only with an unprecedented workload during the pandemic, but also with moral dilemmas, isolation, a lack of psychosocial support and ongoing uncertainty. Studies from various countries show a significant increase in burnout symptoms, especially in high-stress care areas such as emergency rooms, intensive care units and nursing homes (García et al., 2021; Khasne et al., 2020; Mo et al., 2020). Existing deficits in healthcare systems became particularly apparent. The crisis revealed massive weaknesses in institutional prevention and highlighted structural deficiencies – such as inadequate psychosocial support services, poor communication at management level and a strong stigma surrounding mental health issues. The long-term effects of these developments not only affect individual well-being, but also jeopardise the quality of care and staff stability in the healthcare system (Friedrich et al., 2022; Rotenstein et al., 2018). The aim of this article is to highlight the effectiveness of two key prevention factors based on a systematic review of current research findings: psychoeducation and destigmatisation. Psychoeducation has been shown to improve mental health literacy, support the early detection of burnout symptoms and strengthen individual coping skills (Wei et al., 2020; Yamaguchi et al., 2019). Destigmatisation, in turn, is essential for breaking down structural barriers to help services and overcoming cultural expectations of excessive demands, silence and perceived weakness (Knaak et al., 2017; Stuart, 2016). The article discusses international examples of good practice (including from Norway, Canada and the United Kingdom) as well as empirical studies on the influence of organisational culture on the mental well-being of professionals. In addition, it analyses the political and institutional framework conditions that enable the sustainable and ly anchoring of preventive measures in the healthcare system. In addition to international findings, the article incorporates a longitudinal study conducted in German-speaking countries by Gaul (2024), which highlights specific developments in burnout symptoms in the post-pandemic context. The study reveals persistent stress, particularly among nursing staff and medical personnel, and underscores the urgency of structured, scientifically based preventive measures. The article thus makes a practice-oriented, evidence-based and policy-relevant contribution to promoting the mental health of healthcare workers and shows how psychoeducational and destigmatising measures can be effectively integrated into training and continuing education, occupational health management and leadership structures.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».