Intergenerational Effect of Maternal Childhood Maltreatment on Next Generation’s Vulnerability to Psychopathology: A Systematic Review With Meta-Analysis
Bibliographic record
Abstract
Many studies have identified the multiple negative consequences of childhood maltreatment on subsequent mental health. However, research on the intergenerational effect of maternal childhood maltreatment has not been systematically synthesized. This meta-analysis aimed to provide a quantitative estimate of the intergenerational effect of maternal childhood maltreatment on their offspring’s psychopathology. Electronic databases and gray literature were searched for English-language prospective cohort studies. Two reviewers independently extracted data and assessed study quality with the Newcastle-Ottawa Scale. This review only included those studies with (1) maternal childhood maltreatment occurring prior to 18 years of age, (2) using a clear and reliable assessment for maltreatment exposure and offspring’s mental health problems prior to age 18. Random-effect models were used to calculate the pooled effect size of maternal childhood maltreatment on offspring’s psychopathology, and meta-regression was used to explore potential confounders. Twelve studies met eligibility criteria. Significant heterogeneity was found across selected studies. Maternal childhood maltreatment was found to have a small but significant effect on the offspring’s depression and internalizing behaviors ( r = .14, 95% confidence interval [.09, .19]). Two moderators were found, maternal depression and ethnicity. Maternal depression reduced the effect size of maternal maltreatment on offspring’s depression and internalizing disorders. The offspring of non-Caucasian mothers who had a history of childhood maltreatment faced a higher risk of mental health problems. There was no evidence of publication bias. This review provides robust evidence to reinforce the need for policies to reduce its occurrence, as it can influence not just one but two or possibly more generations.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| 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".