Intergenerational Transmission of Child Abuse and Neglect: A Transdisciplinary Analysis
Bibliographic record
Abstract
Parents who experienced maltreatment in childhood may be at an increased risk of presenting abusive or neglectful behavior toward their own children. Research suggests reoccurring patterns of abuse and neglect across generations, but the factors that influence these behavioral patterns are complex and poorly understood. Although abusive or neglectful parenting styles undoubtedly are strongly influenced by social factors (ie, “nurture”), there might be underlying biological components to perpetuating behavioral patterns (ie, “nature”). Importantly, nature and nurture are known to interact in shaping developmental outcomes, and as such should not be considered in isolation. In this review, we examine the evidence regarding the inheritance of abusive and/or neglectful parenting behavior using a transdisciplinary approach. We integrate knowledge of the social and biological science fields on the continuance of abusive and neglectful behavior, as well as the methodological challenges that complicate the interpretation of existing research. Finally, we stress the importance of considering contextual factors of both social and biological research findings concerning the intergenerational inheritance of child abuse and neglect and discuss the potential for early social intervention to disrupt harmful intergenerational patterns.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".