Testing the cycle of maltreatment hypothesis: Meta-analytic evidence of the intergenerational transmission of child maltreatment
Bibliographic record
Abstract
It has long been claimed that "maltreatment begets maltreatment," that is, a parent's history of maltreatment increases the risk that his or her child will also suffer maltreatment. However, significant methodological concerns have been raised regarding evidence supporting this assertion, with some arguing that the association weakens in samples with higher methodological rigor. In the current study, the intergenerational transmission of maltreatment hypothesis is examined in 142 studies (149 samples; 227,918 dyads) that underwent a methodological quality review, as well as data extraction on a number of potential moderator variables. Results reveal a modest association of intergenerational maltreatment (k = 80; d = 0.45, 95% confidence interval; CI [0.37, 0.54]). Support for the intergenerational transmission of specific maltreatment types was also observed (neglect: k = 13, d = 0.24, 95% CI [0.11, 0.37]; physical abuse: k = 61, d = 0.41, 95% CI [0.33, 0.49]; emotional abuse: k = 18, d = 0.57, 95% CI [0.43, 0.71]; sexual abuse: k = 18, d = 0.39, 95% CI [0.24, 0.55]). Methodological quality only emerged as a significant moderator of the intergenerational transmission of physical abuse, with a weakening of effect sizes as methodological rigor increased. Evidence from this meta-analysis confirms the cycle of maltreatment hypothesis, although effect sizes were modest. Future research should focus on deepening understanding of mechanisms of transmission, as well as identifying protective factors that can effectively break the cycle of maltreatment.
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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 | 0.121 | 0.305 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".