Keywords
mental health
principal component analysis
beck depression inventory
Occupational Psychology
How to Cite
Abstract
Introduction: The COVID-19 pandemic exposed nursing professionals to high levels of stress, exacerbating depressive symptoms and emotional exhaustion. Objectives: This study aimed to identify the psychological dimensions associated with depressive symptoms in these professionals during the pandemic using Principal Component Analysis (PCA). Methodology: A cross-sectional, quantitative study was conducted with 290 nursing professionals from a public hospital in Minas Gerais, Brazil. Data were collected online between November 2022 and February 2023 using the Beck Depression Inventory (BDI). Results: The PCA revealed three main components explaining 60.77% of the total variance. The first component (PC1), accounting for 46.96%, captured physical and emotional exhaustion, characterized by fatigue and sleep disturbances. The second component (PC2), explaining 7.71%, reflected emotional perception, including guilt and somatic manifestations. The third component (PC3), accounting for 6.10%, highlighted behavioral changes such as irritability and negative self-image. Discussion: These findings underscore the urgent need for psychological support interventions, workload reduction strategies, and resilience enhancement. Conclusion: The study emphasizes the importance of advanced statistical tools like PCA in understanding complex psychosocial phenomena and provides critical insights for public policies aimed at improving the well-being of healthcare professionals during health crises.
References
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