Maternal depressive symptoms and early childhood cognitive development: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Previous findings have been mixed regarding the relationship between maternal depressive symptoms and child cognitive development. The objective of this study was to systematically review relevant literature and to perform a meta-analysis. METHOD: Three electronic databases (PubMed, EMBASE, PsycINFO) were searched. Initial screening was conducted independently by two reviewers. Studies selected for detailed review were read in full and included based on a set of criteria. Data from selected studies were abstracted onto a standardized form. Meta-analysis using the inverse variance approach and random-effects models was conducted. RESULTS: The univariate analysis of 14 studies revealed that maternal depressive symptoms are related to lower cognitive scores among children aged ⩽56 months (Cohen's d = -0.25, 95% CI -0.39 to -0.12). The synthesis of studies controlling for confounding variables showed that the mean cognitive score for children 6-8 weeks post-partum whose mothers had high depressive symptoms during the first few weeks postpartum was approximately 4.2 units lower on the Mental Developmental Index (MDI) of the Bayley Scales of Infant and Toddler Development (BSID) compared with children with non-symptomatic mothers (B̂ = -4.17, 95% CI -8.01 to -0.32). CONCLUSIONS: The results indicated that maternal depressive symptoms are related to lower cognitive scores in early infancy, after adjusting for confounding factors. An integrated approach for supporting child cognitive development may include program efforts that promote maternal mental health in addition to family economic wellbeing, responsive caregiving, and child nutrition.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".