The Impacts of Prenatal Mental Health Issues on Birth Outcomes during the COVID-19 Pandemic: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The severity of the COVID-19 pandemic is likely to exacerbate mental health problems during the prenatal period and increase the risk of adverse birth outcomes. This review assessed the published literature related to the impacts of prenatal mental health issues on birth outcomes during the COVID-19 pandemic. METHODS: This scoping review was conducted using PROSPERO, Cochrane Library, OVID Medline, Ovid EMBASE, OVID PsycInfo, EBSCO CINAHL, and SCOPUS. The search was conducted using controlled vocabulary and keywords representing the concepts "COVID19", "mental health" and "birth outcomes". The main inclusion criteria were peer-reviewed published articles from late 2019 to the end of July 2021. RESULTS AND DISCUSSION: After removing duplicates, 642 articles were identified, of which two full texts were included for analysis. Both articles highlighted that pregnant women have experienced increasing prenatal mental health issues during the COVID-19 pandemic and, further, increased the risk of developing adverse births. This scoping review highlighted that there is a lack of research on the impact of prenatal mental health issues on birth outcomes during the pandemic. CONCLUSION: Given the severity of the COVID-19 pandemic and the burdens of prenatal mental health issues and adverse birth outcomes, there is an urgent need to conduct further research.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".