Maternal Mental Health in Pregnancy and Its Impact on Children’s Cognitive Development at 18 Months, during the COVID-19 Pandemic (CONCEPTION Study)
Bibliographic record
Abstract
Background: The COVID-19 pandemic has significantly affected the mental health of pregnant persons. Objective: We aimed to evaluate the impact of maternal mental health and antidepressant use on children’s cognitive development. Methods: We followed a cohort of children born during the COVID-19 pandemic. Maternal mental health was self-reported during pregnancy (Edinburgh Postnatal Depression Scale, General Anxiety Disorder-7, stress levels, and antidepressant use). The child’s cognitive development was measured using the third edition of the Ages & Stages Questionnaires® (ASQ-3) at 18 months. Multivariate multinomial logistic regression models were built to assess the association between in utero exposure to maternal mental health and ASQ-3 domains: communication, gross motor, fine motor, problem-solving, and personal–social. Results: Overall, 472 children were included in our analyses. After adjusting for potential confounders, a need for further assessment in communication (adjusted odds ratio (aOR) 12.2, 95% confidence interval (CI) (1.60;92.4)), and for improvement in gross motricity (aOR 6.33, 95%CI (2.06;19.4)) were associated with in utero anxiety. The need for improvement in fine motricity (aOR 4.11, 95%CI (1.00; 16.90)) was associated with antidepressant exposure. In utero depression was associated with a decrease in the need for improvement in problem solving (aOR 0.48, 95%CI (0.24; 0.98)). Conclusions: During the COVID-19 pandemic, maternal mental health appears to be associated with some aspects of children’s cognitive development.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".