Prevalence of anxiety and depression among pregnant women during the COVID-19 pandemic: a meta-analysis
Bibliographic record
Abstract
PURPOSE: Coronavirus disease (COVID-19) is a newly emerged respiratory illness, which has spread around the world. Pregnant women are exposed to additional pressure due to the indirect adverse effects of this pandemic on their physical and mental health. Since the psychological wellness framework is weak in developing countries, it is likely that geographical factors affect the prevalence. Therefore, the goal of this meta-analysis is to investigate the prevalence of anxiety and depression among pregnant women during the COVID-19 pandemic. METHODS: We searched databases including PubMed/MEDLINE, Web of Science, Cochrane Library for articles. The quality of studies was determined based on the STROBE checklist. I2 and Cochrane Q-test were used to determine heterogeneity. Fixed effects and/or random effects models were also employed to estimate pooled prevalence. RESULTS: ) for depression. The results of continent subgroup analysis showed that the prevalence of anxiety was higher in western country (38%) than in Asia country (7.8%). The prevalence of anxiety in Italy (38%), Canada (56%), Pakistan (14%), Greece (53%), Sri Lanka (17.5%), and China (0.3-29%) and Iran 3.8% as well as the prevalence of depression in Canada (37%), Belgium (25%), Turkey (35.4%), Sri Lanka (19.5%), and China (11-29%) has been reported. CONCLUSION: Covid-19 may impose extra pressure on the emotional wellbeing of pregnant women. Therefore, there is an urgent need for resources to help mitigate anxiety and depression in pregnant women.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.071 |
| Bibliometrics | 0.006 | 0.006 |
| 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".