Depression Prevalence of Healthcare Workers During the First Wave of the COVID-19 Pandemic and Its Affecting Variables: A Meta-Analysis
Bibliographic record
Abstract
Purpose: This meta-analysis aimed to systematically review the affecting variables regarding the prevalence of depression in healthcare workers during the COVID-19 pandemic. Method: MedLine, PubMed, Web of Science (Wos), and GoogleScholar databases were searched until June 19, 2020. The quality of studies included was evaluated with The Newcastle-Ottawa Scale. Data were analyzed using Comprehensive Meta-Analysis Version 3.0. The pooled prevalence of depression was interpreted according to the random-effects model. The heterogeneity of the studies was evaluated with Cochran's Q test and I2 statistics. Results: A meta-analysis of depression prevalence in healthcare workers was carried out with 8 studies. Studies with high-quality assessments were analyzed. In this study, which was conducted with a total of 9,841 healthcare workers, the overall depression rate was 40.8% (95% confidence interval [CI] 33.5-48.6; I2=96.48%). In the subgroup analysis to determine the influencing variables, the rate of depression in female healthcare workers was 24.5% (95% CI: 17.4–33.3) and the rate of depression in male healthcare workers was 8.5% (95% CI: 5.5–12.7). In addition, the depression rate was 43.6% (95% CI: 35.9–51.7) in studies conducted in China and 18.5% (95% CI: 7.5–38.7) in a study conducted in Korea. No statistically significant difference was found as a result of the subgroup analysis in terms of profession, the measurement tool and the period of time (p>0.05). Conclusion: This meta-analysis provides evidence that 4 out of 10 healthcare workers experience depression during the COVID-19 pandemic, with country and gender as the most influencing variable, respectively.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.078 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".