Mental health in pregnant individuals during the COVID-19 pandemic based on a Swiss online survey
Bibliographic record
Abstract
The aim of our study was to evaluate the mental health of pregnant individuals during the early COVID-19 pandemic and the potential factors associated. A Swiss online survey was proposed to individuals who gave birth during the pandemic period from March 2020. The Edinburgh Postnatal Depression Scale (EPDS), Generalized Anxiety Disorder 7 questions (GAD-7), and Impact Event Scale-Revised (IES-R) were evaluated and used to defined mental health impairment as a composite outcome. From October, 2020 to February, 2021, 736 participants responded. The anxiety GAD-7 score was moderate in 9.6% and severe in 2.0%. The EPDS was moderate in 21.5% and severe in 32.9%. The IES-R was moderate in 10.3% and severe in 3.9%. Mental health impairment was reported in 37.0%. The association between the risk of mental health impairment and foreign nationality was significant (OR = 1.48; 95%CI [1.06-2.05]) as well as fetal and pregnancy worries because of coronavirus (OR = 1.46; 95% CI [1.08-1.98]) and 1.65; 95% CI [1.22-2.24]). Adjusted ORs were significant for foreign nationality (aOR = 1.51; 95%CI [1.07-2.13]) and pregnancy worries because of coronavirus (aOR = 1.62; 95%CI [1.10-2.40]). Pregnant people and especially foreign national have a high risk of mental health impairment during the pandemic.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".