The COVID-19 Pandemic Impacted Maternal Mental Health Differently Depending on Pregnancy Status and Trimester of Gestation
Bibliographic record
Abstract
Introduction: We aimed to measure the impact of the COVID-19 pandemic on maternal mental health, stratifying on pregnancy status, trimester of gestation, and pandemic period/wave. Methods: Pregnant persons and persons who delivered in Canada during the pandemic, >18 years, were recruited, and data were collected using a web-based strategy. The current analysis includes data on persons enrolled between 06/2020−08/2021. Maternal sociodemographic indicators, mental health measures (Edinburgh Perinatal Depression Scale (EPDS), Generalized Anxiety Disorders (GAD-7), stress) were self-reported. Maternal mental health in pregnant women (stratified by trimester, and pandemic period/wave at recruitment) was compared with the mental health of women who had delivered; determinants of severe depression were identified with multivariate logistic regression models. Results: 2574 persons were pregnant and 626 had already delivered at recruitment. Participants who had delivered had significantly higher mean depressive symptom scores compared to those pregnant at recruitment (9.1 (SD, 5.7) vs. 8.4 (SD, 5.3), p = 0.009). Maternal anxiety (aOR 1.51; 95%CI 1.44−1.59) and stress (aOR 1.35; 95%CI 1.24−1.48) were the most significant predictors of severe maternal depression (EDPS ˃ 13) in pregnancy. Conclusion: The COVID-19 pandemic had a significant impact on maternal depression during pregnancy and in the post-partum period. Given that gestational depression/anxiety/stress has been associated with preterm birth and childhood cognitive problems, it is essential to continue following women/children, and develop strategies to reduce COVID-19′s longer-term impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".