Sex differences in the association between maternal depression and child and adolescent cognitive development: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Maternal depression is negatively associated with cognitive development across childhood and adolescence, with mixed evidence on whether this association differs in boys and girls. Herein, we performed a systematic review and meta-analysis of sex-specific estimates of the association between maternal depression and offspring cognitive outcomes. METHOD: Seven databases (PubMed, EMBASE, PsycINFO, ERIC, CINAHL, Scopus, ProQuest) were searched for studies examining the longitudinal association between maternal depression and offspring (up to 18 years) cognitive outcomes. Studies were screened and included based on predetermined criteria by two independent reviewers (Cohen's κ = 0.76). We used random-effects models to conduct a meta-analysis and used meta-regression for subgroup analyses. The PROSPERO record for the study is CRD42020161001. RESULTS: Twelve studies met inclusion criteria. Maternal depression was associated with poorer cognitive outcomes in boys [Hedges' g = -0.36 (95% CI -0.60 to -0.11)], but not in girls [-0.17 (-0.41 to 0.07)]. The association in boys varied as a function of the measure of depression used (b = -0.70, p = 0.005): when maternal depression was assessed via a diagnostic interview, boys [-0.84 (-1.23 to -0.44)] had poorer cognitive outcomes than when a rating scale was used [-0.16 (-0.36 to 0.04)]. CONCLUSIONS: This review and meta-analysis indicates that maternal depression is only significantly associated with cognitive outcomes in boys. Understanding the role of sex differences in the underlying mechanisms of this association can inform the development of targeted interventions to mitigate the negative effects of maternal depression on offspring cognitive outcomes.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.005 | 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 | 0.001 |
| 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".