Background And Aim: The COVID-19 pandemic has led to a significant adverse effect on the mental health of healthcare professionals. This study aims to assess the effects of the prolonged pandemic on burnout and mood disorders and to evaluate the influence of positive vaccination beliefs on these factors at a medical center during the extended COVID-19 pandemic.
Methods: This retrospective study analyzed the results of an online questionnaire survey including burnout status and mood disorders from 2020 to 2022. The factors related to mood moderate/severe disorders and the impact of the positive vaccine belief were also explored.
Results: The initial analysis revealed that healthcare professionals continued to experience significant levels of personal and work-related burnout, along with mood disorders. However, the scores and the percentage of moderate to severe burnout gradually decreased. Notably, the percentage of individuals with moderate to severe mood disorders also gradually declined (2020: 13.4%, 2021: 12.3%, 2022: 11.1%). The number of participants who need professional interventions decreased from 56.2% in 2020 to 45.9% in 2021, and 46% in 2022. Multivariate analysis revealed a positive vaccine belief was associated with a lower risk of moderate/severe mood disorders, with odd ratios (OR) and 95% confidence intervals (95% CI) of 0.38 (0.28 - 0.52) and 0.41 (0.30 - 0.52) in the 2021 and 2022 cohorts, respectively. Further investigation revealed that age over 50 was linked to a positive vaccine belief in 2021 and 2022. Within the 2022 cohort, working as nurses was identified as the independent factor associated with a less positive belief, with the OR and 95% CI of 0.49 (0.27 - 0.90).
Conclusion: The findings of the present study suggest burnout and mood disorders are still significant during the pandemic. A positive vaccine belief may mitigate pandemic-related mental distress. Further interventions to enhance the belief combined with other supporting measures are important in a long fight against the pandemic.
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http://dx.doi.org/10.3389/fpsyt.2024.1402194 | DOI Listing |
Sci Data
January 2025
University of Bergen, Department of Clinical Medicine, Bergen, 5009, Norway.
Mental health is vital to human well-being, and prevention strategies to address mental illness have a significant impact on the burden of disease and quality of life. With the recent developments in body-worn sensors, it is now possible to continuously collect data that can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment, thereby improving patient experiences and quality of life.
View Article and Find Full Text PDFPersonal Ment Health
February 2025
Fédération Régionale de Recherche en Santé Mentale et Psychiatrie des Hauts-de-France, Saint-André-Lez-Lille, France.
Borderline personality disorder (BPD) is a frequent disorder with high mental health care utilization. This study aims to describe BPD hospitalization in France: using the French national hospitals database from 2013 to 2022, regarding sociodemographic factors and hospitalization characteristics. In total, this study included 121,235 patients.
View Article and Find Full Text PDFBackground: Childhood sleep problems are common and impact physical and emotional health. Prior work suggests that prenatal maternal depression and anxiety associate with disturbed child sleep in infancy. The current study evaluated whether these same associations extend to children at 3 years of age, and if so, whether the timing of symptoms in pregnancy is relevant.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
Introduction: While the fact that visual stimuli synthesized by Artificial Neural Networks (ANN) may evoke emotional reactions is documented, the precise mechanisms that connect the strength and type of such reactions with the ways of how ANNs are used to synthesize visual stimuli are yet to be discovered. Understanding these mechanisms allows for designing methods that synthesize images attenuating or enhancing selected emotional states, which may provide unobtrusive and widely-applicable treatment of mental dysfunctions and disorders.
Methods: The Convolutional Neural Network (CNN), a type of ANN used in computer vision tasks which models the ways humans solve visual tasks, was applied to synthesize ("dream" or "hallucinate") images with no semantic content to maximize activations of neurons in precisely-selected layers in the CNN.
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