A longitudinal study examining the associations between prenatal and postnatal maternal distress and toddler socioemotional developmental during the COVID‐19 pandemic
Bibliographic record
Abstract
Elevated psychological distress, experienced by pregnant women and parents, has been well-documented during the COVID-19 pandemic. Most research focuses on the first 6-months postpartum, with single or limited repeated measures of perinatal distress. The present longitudinal study examined how perinatal distress, experienced over nearly 2 years of the COVID-19 pandemic, impacted toddler socioemotional development. A sample of 304 participants participated during pregnancy, 6-weeks, 6-months, and 15-months postpartum. Mothers reported their depressive, anxiety, and stress symptoms, at each timepoint. Mother-reported toddler socioemotional functioning (using the Brief Infant-Toddler Social and Emotional Assessment) was measured at 15-months. Results of structural equation mediation models indicated that (1) higher prenatal distress was associated with elevated postpartum distress, from 6-weeks to 15-months postpartum; (2) associations between prenatal distress and toddler socioemotional problems became nonsignificant after accounting for postpartum distress; and (3) higher prenatal distress was indirectly associated with greater socioemotional problems, and specifically elevated externalizing problems, through higher maternal distress at 6 weeks and 15 months postpartum. Findings suggest that the continued experience of distress during the postpartum period plays an important role in child socioemotional development during the COVID-19 pandemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".