Maternal Adverse Childhood Experiences and Infant Development
Bibliographic record
Abstract
OBJECTIVES: To examine the prenatal and postnatal mechanisms by which maternal adverse childhood experiences (ACEs) predict the early development of their offspring, specifically via biological (maternal health risk in pregnancy, infant health risk at birth) and psychosocial risk (maternal stress during and after pregnancy, as well as hostile behavior in early infancy). METHODS: Participants were 1994 women (mean age = 31 years) and their infants, who were recruited in pregnancy as part of a prospective longitudinal cohort from 2008 to 2010. Pregnant women completed self-report questionnaires in pregnancy and postpartum related to psychosocial risk and a questionnaire about hostile behavior when their infant was 4 months of age. Health risk in pregnancy and infant health risk at birth were obtained from health records. Mothers completed the Ages and Stages Questionnaire when infants were 12 months of age. RESULTS: Path analysis revealed that the association between maternal ACEs and infant development outcomes at 12 months operated through 2 indirect pathways: biological health risk (pregnancy health risk and infant health risk at birth) and psychosocial risk (maternal psychosocial risk in pregnancy and maternal hostile behavior in infancy). CONCLUSIONS: Psychosocial risks in pregnancy, but not in early infancy, contribute to the transmission of vulnerability from maternal ACEs to child development outcomes in infancy via maternal behavior. Maternal health risk in pregnancy indirectly confers risk from maternal ACEs to child development outcomes at 12 months of age through infant health risk. Maternal health and psychosocial well-being in pregnancy may be key targets for intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".