Prevalence of Anxiety and Depression in Iranian Health Care Workers during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Covid-19 pandemic has imposed a significant effect on mental health of the health care workers. The present systematic review and meta-analysis aimed at determining the pooled prevalence of anxiety and depression among Iranian health care workers during the Covid-19 pandemic. Material and Methods: To conduct this systematic review and meta-analysis, Web of Science, Scopus, Medline (PubMed), Embase, SID, Magiran databases and Google Scholar search engine were investigated to find studies over the prevalence of anxiety and depression among health care workers during the Covid-19 pandemic from December 2019 to June 10, 2021. Quality of the primary studies was assessed using the Newcastle-Ottawa tool and the random effects model was applied to estimate the pooled prevalence. Furthermore, χ2 test and I2 index were used to evaluate the degree of heterogeneity among the studies. The pooled prevalence of anxiety and depression in different subgroups was reported based on the severity of anxiety and depression, assessment tools, and staff jobs. Results: Of 488 articles obtained as a result of the initial search, 10 related studies were identified and entered into the systematic review and meta-analysis. The pooled prevalence of anxiety was 42% (95% CI: 25-75) and the pooled prevalence of depression was 35% (95% CI: 19-55). The pooled prevalence of anxiety was 54% (95% CI: 39-70) in the occupational group of nurses and 29% (95% CI: 17-44) among all health care workers. The pooled prevalence of depression was 46% (95% CI: 30 to 63) and 17% (95% CI: 10 to 26) among nurses and all health workers, respectively. Conclusion: According to the findings, a high prevalence of anxiety and depression was observed among the health care workers of Iran during the Covid-19 epidemic. The authorities are required to plan for preventive and therapeutic interventions to reduce the psychological burden of the epidemic.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.010 | 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".