Parental adverse childhood experiences and offspring neurobehavioral functioning
Bibliographic record
Abstract
Abstract Background Mental health conditions in children include diagnoses and transdiagnostic characteristics A variety of factors such as child exposure to violence and neglect contribute to mental health conditions in children. However, less is known about how parental exposure to adverse childhood experiences (ACEs), such as domestic violence, neglect, and abuse, impacts their children. We aim to systematically synthesize available data on the association of parental ACEs with child mental health characteristics. Methods We conducted a systematic review and meta-analysis using searches across MEDLINE, Embase, CINAHL, APA PsycInfo, Scopus, and SciELO. Studies published up to September 2024 were included if they examined parental ACEs in relation to children's mental health. Meta-analyses used random effects models, conducted in STATA. Results The search yielded 6,270 articles (after removing duplicates), with 173 full-text reviewed, and 52 meeting inclusion criteria. Samples sizes ranged from 50 to 8,473 parent-child dyads (M = 764.89, SD = 1,446.95); median child age was 36 months at the time of outcome assessment. Studies were mainly from the U.S. and Canada. Parental ACEs were significantly associated with child mental health conditions (Beta: 0.01, 95% CI: 0.03, 0.05), particularly child behavioral dysregulation (Beta: 0.22, 95% CI 0.16, 0.28) and socio-emotional difficulties (Beta: 0.16, 95% CI: 0.00, 0.31). Parental ACEs were also linked to reduced child emotion regulation (Beta -0.07, 95% CI: -0.14, 0.00), and reduced child executive function (Beta -0.02, 95% CI: -0.06, 0.02). Conclusions Parental ACEs are associated with various offspring mental health conditions. Child mental health characteristics should be investigated with a transdiagnostic perspective focusing on transdiagnostic child mental health characteristics beyond diagnoses. More international studies investigating the association of parental ACEs on offspring's mental health conditions are needed.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".