BURNOUT IN HEALTHCARE AFTER THE PANDEMIC: PSYCHOEDUCATION AND DESTIGMATISATION AS KEYS TO PREVENTION
Bibliographic record
Abstract
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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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".