Evaluation of psychological distress, burnout and structural empowerment status of healthcare workers during the outbreak of coronavirus disease (COVID-19): a cross-sectional questionnaire-based study
Bibliographic record
Abstract
BACKGROUND: In light of several recent studies, there is evidence that the coronavirus disease 2019 (COVID-19) pandemic has caused various mental health concerns in the general population, as well as among healthcare workers (HCWs). The main aim of this study was to assess the psychological distress, burnout and structural empowerment status of HCWs during the COVID-19 outbreak, and to evaluate its predictors. METHODS: This multi-center, cross-sectional web-based questionnaire survey was conducted on HCWs during the outbreak of COVID-19 from August 2020 to January 2021. HCWs working in hospitals from 48 different countries were invited to participate in an online anonymous survey that investigated sociodemographic data, psychological distress, burnout and structural empowerment (SE) based on Depression Anxiety and Stress Scale 21 (DASS-21), Maslach Burnout Inventory (MBI) and Conditions for work effectiveness questionnaire (CWEQ_II), respectively. Predictors of the total scores of DASS-21, MBI and CWEQ-II were assessed using unadjusted and adjusted binary logistic regression analysis. RESULTS: Out of the 1030 HCWs enrolled in this survey, all completed the sociodemographic section (response rate 100%) A total of 730 (70.9%) HCWs completed the DASS-21 questionnaire, 852 (82.6%) completed the MBI questionnaire, and 712 (69.1%) completed the CWEQ-II questionnaire. The results indicate that 360 out of 730 responders (49.3%) reported severe or extremely severe levels of stress, anxiety, and depression. Additionally, 422 out of 851 responders (49.6%) reported a high level of burnout, while 268 out of 712 responders (37.6%) reported a high level of structural empowerment based on the DASS-21, MBI, and CWEQ-II scales, respectively. In addition, the analysis showed that HCWs working in the COVID-19 areas experienced significantly higher symptoms of severe stress, anxiety, depression and higher levels of burnout compared to those working in other areas. The results also revealed that direct work with COVID-19 patients, lower work experience, and high workload during the outbreak of COVID-19 increase the risks of negative psychological consequences. CONCLUSION: Health professionals had high levels of burnout and psychological symptoms during the COVID-19 emergency. Monitoring and timely treatment of these conditions is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".