Prenatal and postpartum maternal mental health and neonatal motor outcomes during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Rates of prenatal and postpartum stress and depression in pregnant individuals have increased during the COVID-19 pandemic. Perinatal maternal mental health has been linked to worse motor development in offspring, with motor deficits appearing in infancy and early childhood. We aimed to evaluate the relationship between prenatal and postpartum stress and depression and motor outcome in infants born during the COVID-19 pandemic. Methods One hundred and seventeen participants completed an online prospective survey study at two timepoints: during pregnancy and within 2 months postpartum. Depression was self-reported using the Edinburgh Perinatal/Postpartum Depression Scale (EPDS), and stress via the Perceived Stress Scale (PSS). Mothers reported total infant motor ability (fine and gross) using the interRAI 0–3 Developmental Domains questionnaire. Results Prenatal (B=-0.035, 95%CI=-0.062 to -0.007, p = .014) and postpartum maternal depression outcome (B=-0.037, 95%CI= -0.066 to -0.008, p = 0.012) were significant negative predictors of total infant motor ability (fine and gross). Neither pregnancy nor postpartum perceived stress predicted infant motor function. A cluster analysis revealed that preterm and low birth weight infants belonging to mothers who reported elevated depressive symptoms during pregnancy and in the postpartum period had the poorest motor outcome. Conclusions Prenatal and postpartum depression, but not stress, predicted infant motor ability during the first 2 months of life. Preterm and low-birth weight infants of mothers who report elevated depressive symptoms are the most at-risk of experiencing poor motor outcome.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".