Caregiver and Child Adverse Childhood Experiences: A Meta-Analysis
Bibliographic record
Abstract
CONTEXT: Exposure to adverse childhood experiences (ACEs) is associated with adverse impacts on subsequent generations. The extent to which caregiver ACEs are associated with their child's ACE score is unclear. OBJECTIVE: To meta-analytically examine the association between caregiver and child ACE score. Potential moderators of this association were explored. DATA SOURCES: Systematic searches were conducted using MEDLINE, Embase, PsycINFO, and CINHAL from 1998, the year the ACEs questionnaire was published, to February 19, 2024. STUDY SELECTION: Inclusion criteria were that the ACEs questionnaire was completed for both caregiver and child, an effect size was available, and the study was published in English. DATA EXTRACTION: Variables extracted included sample size and magnitude of association between caregiver ACEs and child ACEs, mean caregiver and child age, sex (% female), race and ethnicity, and informant of ACEs. RESULTS: Seventeen samples (4872 caregiver-child dyads) met inclusion criterion. Results revealed a large pooled-effect size between caregiver and child ACEs (r = 0.33; 95% CI, 0.25-0.41; P < .001), such that higher caregiver ACEs score was associated with higher child ACEs score. This association was stronger among studies with younger caregivers and studies that utilized caregiver-report compared with child self-report of ACEs. LIMITATIONS: Many studies were conducted in North America with female caregiver samples, limiting generalizability beyond these populations. CONCLUSIONS: Caregiver ACEs were strongly associated with child ACEs. Prevention and intervention efforts for caregivers should be trauma informed and focused on bolstering protective factors that may break cycles of intergenerational risk.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".