Prenatal maternal mental health and resilience in the United Kingdom during the SARS-CoV-2 pandemic: a cross- national comparison
Bibliographic record
Abstract
Introduction: Prenatal mental health problems are associated with morbidity for the pregnant person, and their infants are at long-term risk for poor health outcomes. We aim to explore how the SARS-CoV-2 pandemic affected the mental health of pregnant people in the United Kingdom (UK), and to further identify resilience factors which may have contributed to varying mental health outcomes. We also aim to examine the quality of antenatal care provided during the pandemic in the UK and to identify potential inadequacies to enhance preparedness for future events. Methods: During June-November 2020, we recruited 3666 individuals in the UK for the EPPOCH pregnancy cohort (Maternal mental health during the COVID-19 pandemic: Effect of the Pandemic on Pregnancy Outcomes and Childhood Health). Participants were assessed for depression, anxiety, anger and pregnancy-related anxiety using validated scales. Additionally, physical activity, social support, individualized support and personal coping ability of the respondents were assessed as potential resilience factors. Results: Participants reported high levels of depression (57.05%), anxiety (58.04%) and anger (58.05%). Higher levels of social and individualized support and personal coping ability were associated with lower mental health challenges. Additionally, pregnant individuals in the UK experienced higher depression during the pandemic than that reported in Canada. Finally, qualitative analysis revealed that restrictions for partners and support persons during medical appointments as well as poor public health communication led to increased mental health adversities and hindered ability to make medical decisions. Discussion: This study revealed increased mental health challenges among pregnant individuals in the UK during the SARS-CoV-2 pandemic. These results highlight the need for reassessing the mental health support measures available to pregnant people in the UK, both during times of crisis and in general.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".