Elevated depression and anxiety among pregnant individuals during the COVID-19 pandemic
Bibliographic record
Abstract
Mental health problems are common in pregnancy, typically affecting between 10-25% of pregnant individuals. Elevated symptoms of depression and anxiety can negatively impact both the pregnant individual and developing fetus. The current COVID-19 pandemic is a unique stressor with potentially wide-ranging consequences for pregnancy and beyond. We assessed symptoms of anxiety and depression among pregnant individuals during the current COVID-19 pandemic and determined factors that were associated with psychological distress. 1987 pregnant participants were surveyed across Canada in April 2020. The assessment included questions about COVID-19-related stress and standardized measures of depression, anxiety, pregnancy-related anxiety, sleep and social support. We found substantially elevated psychological distress compared to similar pre-pandemic pregnancy cohorts, with 37% reporting clinically relevant symptoms of depression, 57% reporting clinically relevant symptoms of anxiety, and 68% reporting elevated pregnancy-related anxiety. Higher levels of social support and longer sleep duration were associated with lower psychological symptoms across domains. This study shows concerningly elevated levels of psychological distress among pregnant individuals during the COVID-19 pandemic, that may have long-term impacts on their children. Potential intervention targets are needed in addition to improving protective factors related to increased social support and sleep -- these should be urgently considered to mitigate long-term negative outcomes.
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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) | 0.001 | 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".