Impact of the COVID-19 pandemic on maternal mental health during pregnancy: The CONCEPTION study – Phase I
Bibliographic record
Abstract
Introduction Mental health regional differences during pregnancy through the COVID-19 pandemic is understudied. Objectives We aimed to quantify the impact of the COVID-19 pandemic on maternal mental health during pregnancy. Methods A cohort study with a web-based recruitment strategy and electronic data collection was initiated in 06/2020. Although Canadian women, >18 years were primarily targeted, pregnant women worldwide were eligible. The current analysis includes data on women enrolled 06/2020-11/2020. Self-reported data included mental health measures (Edinburgh Perinatal Depression Scale (EPDS), Generalized Anxiety Disorders (GAD-7)), stress. We compared maternal mental health stratifying on country/continents of residence, and identified determinants of mental health using multivariable regression models. Results Of 2,109 pregnant women recruited, 1,932 were from Canada, 48 the United States (US), 73 Europe, 35 Africa, and 21 Asia/Oceania. Mean depressive symptom scores were lower in Canada (EPDS 8.2, SD 5.2) compared to the US (EPDS 10.5, SD 4.8) and Europe (EPDS 10.4, SD 6.5) (p<0.05), regardless of being infected or not. Maternal anxiety, stress, decreased income and access to health care due to the pandemic were increasing maternal depression. The prevalence of severe anxiety was similar across country/continents. Maternal depression, stress, and earlier recruitment during the pandemic (June/July) were associated with increased maternal anxiety. Conclusions In this first international study on the impact of the COVID-19 pandemic, CONCEPTION has shown significant country/continent-specific variations in depressive symptoms during pregnancy, whereas severe anxiety was similar regardless of place of residence. Strategies are needed to reduce COVID-19’s mental health burden in pregnancy. Disclosure No significant relationships.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".