Prevalence of Psychological Disorders among Health Workers During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Repeated contact with patients with COVID-19 and working in quarantine conditions has made health workers vulnerable to psychological distress during the COVID-19 pandemic. The goal of the present systematic review and meta-analysis was to examine the prevalence of the various psychological distresses among health workers during the COVID-19 pandemic. Methods: PubMed, Scopus, Web of Science, EMBASE, and Cochrane databases were searched for access to papers examining psychological distress among healthcare workers during the COVID-19 pandemic. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). Heterogeneity among the studies was examined using the Cochran's Q test; because heterogeneity was significant, the random effects model was used to examine the prevalence of psychological distress. Results: Overall, 12 studies with a total sample size of 5265 were eligible and included in the analysis. Prevalence rates of depression, anxiety, and PTSD were 20% (95% CI: 14-27), 23% (95% CI: 18-27), and 8% (95% CI: 6-9), respectively. The highest prevalence rates of depression and anxiety were related to the SDS and the GAD-7, respectively, and the lowest prevalence rates of the two aforementioned variables were related to the DASS-21. Conclusions: The high prevalence of psychological distress among healthcare workers during the COVID-19 epidemic can have negative effects on their health and the quality of services provided. Therefore, training coping strategies for psychological distress in this pandemic seems necessary.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".