Maternal Depression in Early Childhood and Developmental Vulnerability at School Entry
Bibliographic record
Abstract
OBJECTIVES: To assess the relation between exposure to maternal depression before age 5 and 5 domains of developmental vulnerability at school entry, overall, and by age at exposure. METHODS: = 52 103). Maternal depression was defined by using physician visits, hospitalizations, and pharmaceutical data; developmental vulnerability was assessed by using the Early Development Instrument. Relative risk of developmental vulnerability was assessed by using log-binomial regression models adjusted for characteristics at birth. RESULTS: Children exposed to maternal depression before age 5 had a 17% higher risk of having at least 1 developmental vulnerability at school entry than did children not exposed to maternal depression before age 5. Exposure to maternal depression was most strongly associated with difficulties in social competence (adjusted relative risk [aRR] = 1.28; 95% confidence interval [CI]: 1.20-1.38), physical health and well-being (aRR = 1.28; 95% CI: 1.20-1.36), and emotional maturity (aRR = 1.27; 95% CI: 1.18-1.37). For most developmental domains, exposure to maternal depression before age 1 and between ages 4 and 5 had the strongest association with developmental vulnerability. CONCLUSIONS: Our finding that children exposed to maternal depression are at higher risk for developmental vulnerability at school entry is consistent with previous findings. We extended this literature by documenting that the adverse effects of exposure to maternal depression are specific to particular developmental domains and that these effects vary depending on the age at which the child is exposed to maternal depression.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".