Socioemotional development in infants of pregnant women during the COVID-19 pandemic: the role of prenatal and postnatal maternal distress
Bibliographic record
Abstract
BACKGROUND: An upsurge in psychological distress was documented in pregnant women during the COVID-19 pandemic. We investigated with a longitudinal design whether prenatal and postnatal maternal distress during the COVID-19 pandemic was associated with lower infant socioemotional development. METHODS: = 30,00, 97.6% White) were recruited during the first COVID-19 mandatory lockdown in Quebec, Canada, from April 2nd to April 13th 2020 and were re-contacted at two months postpartum to complete self-reported measures of general (i.e. not specifically related to the COVID-19 pandemic) anxio-depressive symptoms and infant development. Structural equation modeling analyses were performed using maximum likelihood parameter estimation. RESULTS: Higher maternal prenatal distress significantly contributed to poorer infant socioemotional development. A mediation model showed that postnatal distress significantly mediated the association between prenatal distress and infant socioemotional development, whereas the direct effect of prenatal distress was no longer significant. Prenatal and postnatal maternal distress accounted for 13.7% of the variance in infant socioemotional development. CONCLUSION: Our results call for special means of clinical surveillance in mothers and for innovative (online) interventions aiming to support maternal mental health during pregnancy and after delivery.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".