Prenatal maternal distress during the COVID-19 pandemic and its effects on the infant brain
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has caused elevated distress in pregnant individuals, which has the potential to impact the developing infant. In this study, we examined anxiety and depression symptoms during the pandemic in a large sample of pregnant individuals (n=8602). For a sub-sample of participants, their infants underwent magnetic resonance imaging (MRI) at 3-months of age to examine whether this prenatal maternal distress was associated with infant brain changes. We found significantly elevated prenatal maternal distress compared to pre-pandemic rates, with 47% and 33% of participants reporting clinically significant symptoms of anxiety and depression, respectively. Importantly, we identified social support as a protective factor for clinically elevated prenatal maternal distress. We found significant relationships between prenatal maternal distress and infant amygdala-prefrontal microstructural and functional connectivity and demonstrate for the first time that social support moderates this relationship. Our findings suggest a potentially long-lasting impact of the COVID-19 pandemic on children and show that social support acts as a protective factor not just for pregnant individuals, but also for their developing infants. These findings provide timely evidence to inform clinical practice and policy surrounding the care of pregnant individuals and highlight the importance of social support.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".