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 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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".