Intergenerational transmission of adverse childhood experiences via maternal depression and anxiety and moderation by child sex
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) of parents are associated with a variety of negative health outcomes in offspring. Little is known about the mechanisms by which ACEs are transmitted to the next generation. Given that maternal depression and anxiety are related to ACEs and negatively affect children's behaviour, these exposures may be pathways between maternal ACEs and child psychopathology. Child sex may modify these associations. Our objectives were to determine: (1) the association between ACEs and children's behaviour, (2) whether maternal symptoms of prenatal and postnatal depression and anxiety mediate the relationship between maternal ACEs and children's behaviour, and (3) whether these relationships are moderated by child sex. Pearson correlations and latent path analyses were undertaken using data from 907 children and their mothers enrolled the Alberta Pregnancy Outcomes and Nutrition study. Overall, maternal ACEs were associated with symptoms of anxiety and depression during the perinatal period, and externalizing problems in children. Furthermore, we observed indirect associations between maternal ACEs and children's internalizing and externalizing problems via maternal anxiety and depression. Sex differences were observed, with boys demonstrating greater vulnerability to the indirect effects of maternal ACEs via both anxiety and depression. Findings suggest that maternal mental health may be a mechanism by which maternal early life adversity is transmitted to children, especially boys. Further research is needed to determine if targeted interventions with women who have both high ACEs and mental health problems can prevent or ameliorate the effects of ACEs on children's behavioural psychopathology.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".