Systematic Review: The Effect of Covid-19 on Anxiety in Pregnant Women
Bibliographic record
Abstract
Abstract \nPregnancy is a very vulnerable period in a woman's life. Hormonal changes during pregnancy can affect emotional instabilityThe anxiety caused will have an impact on the health of mothers and children such as the risk of preeclampsia, premature birth, low birth weight, and fetal growth restriction. This study aims to determine the existing literature on the impact of COVID-19 on anxiety in pregnant women. This study used a systematic literature review method. The population in this study were journals from the Pubmed, Science Direct, Sage, Emerald, and Proquest databases published between 2020 and 2021. Result of study was a total of 15 of the 675 articles met the inclusion criteria. This study found that 8 out of 15 articles experienced an increase in the prevalence of anxiety in pregnant women by more than half a percent (90.5%; 57.8%; 77%; 64.5%) and experienced an increase in anxiety prevalence by more than a quarter percent ( 43.9%; 46.3%; 25.6%; 37.5%. In addition, this study found differences in the anxiety of pregnant women between before COVID-19 and during COVID-19 Pandemics. Anxiety in pregnant women included gestational age, demographics, socioeconomic status, knowledge, social support, and physical activity. The COVID-19 pandemic could increase anxiety in pregnant women. Mental health of pregnant women should be one of the priorities in public health to improve the welfare of pregnant women. Policymakers and health planners need to consider mental health in pregnant women in designing procedures to deal with the COVID-19 pandemic.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".