Maternal Postnatal Depressive Symptoms and Early Achievement of Developmental Milestones in Infants and Young Children: A Meta-Analysis
Bibliographic record
Abstract
Screening for social determinants of health, including maternal depression, has become a common and recommended pediatric practice. However, the magnitude of association between maternal and child screening tools remains to be determined. The current study evaluated the association between maternal postnatal depressive symptoms and child developmental milestones, as well as moderators of these associations. A comprehensive search strategy was carried out in four databases (MEDLINE, EMBASE, APA PsycINFO, and Cochrane Central Register of Controlled Trials) from database inception to September 2022. Studies that examine postnatal depressive symptoms and associations with infant and early child (<6 years) achievement of developmental milestones were included. Data were extracted by two independent coders and a random-effects meta-analysis was used to estimate pooled effect sizes and test for moderators. A total of 38 non-overlapping studies (95,897 participants), all focused on maternal postnatal depression, met inclusion criteria. Random-effect meta-analyses were conducted. The pooled effect size for the association between postnatal depressive symptoms and early achievement of infant and child developmental milestones (N = 38; r =-.12; 95% CI= -.18, -.06) was small in magnitude. Older child age at maternal depression measurement was a moderator. Maternal postnatal depressive symptoms are one early indicator of early achievement of developmental milestones and should be included in screening during routine child wellness visits.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| 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".