Maternal social support during and after pregnancy and child cognitive ability: examining timing effects in two cohorts
Bibliographic record
Abstract
BACKGROUND: Maternal anxiety, depression, and stress during and after pregnancy are negatively associated with child cognitive development. However, the contribution of positive maternal experiences, such as social support, to child cognitive development has received less attention. Furthermore, how maternal experience of social support during specific developmental periods impacts child cognitive development is largely unknown. METHODS: = 420), we investigated the associations between maternal perceived social support during and after pregnancy and child's general cognitive ability at 8 years of age, assessed with the Wechsler Intelligence Scale for Children (WISC). Bayesian relevant life course modeling was used to investigate timing effects of maternal social support on child cognitive ability. RESULTS: In both cohorts, higher maternal perceived social support during pregnancy was associated with higher performance on the WISC, independent of sociodemographic factors and concurrent maternal symptoms of depression and anxiety. In ALSPAC, pregnancy emerged as a sensitive period for the effects of perceived social support on child cognitive ability, with a stronger effect of social support during pregnancy than after pregnancy on child cognitive ability. CONCLUSIONS: Our findings, supported from two prospective longitudinal cohorts, suggest a distinct role of maternal perceived social support during pregnancy for cognitive development in children. Our study suggests that interventions aimed at increasing maternal social support during pregnancy may be an important strategy for promoting maternal and child well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".