Intergenerational Cycles of Maltreatment: An Updated Scoping Review of Psychosocial Risk and Protective Factors
Bibliographic record
Abstract
The intergenerational continuity of child maltreatment (CM) is a growing public health concern. Identifying modifiable risk and protective factors involved in these cycles is crucial. A previous scoping review synthesized the literature on psychosocial factors associated with intergenerational CM up to 2018. Since then, a sizable number of studies have been published; this updated review aims to summarize this recent literature. We conducted a comprehensive search across five major databases (PsycINFO, Scopus, Medline, Social Work Abstracts, and ProQuest Dissertations/Theses) from November 2018 to November 2023. The primary inclusion criterion was documentation of intergenerational maltreatment, with studies reporting at least one psychosocial risk or protective factor. Included studies involved human participants, presented original findings, were written in English or French, and employed any research design. This updated review included 29 new studies. Findings indicate that caregivers' individual (e.g., sociodemographic characteristics, psychopathology), relational (e.g., IPV, attachment), contextual (e.g., socioeconomic disadvantage), and historical factors (e.g., cumulative CM, out-of-home placement), along with characteristics of the second generation (e.g., sociodemographic characteristics, psychopathology), are involved in the intergenerational continuity of CM. The implications for practice suggest targeted interventions should address depression, PTSD, and emotional dysregulation in CM survivors, along with fostering secure, supportive family relationships, and positive parenting skills. Policy implications emphasize the need for enhanced support for child protection services in early CM identification, public policies to combat poverty, equitable childcare responsibilities, and funding for research in low-to-middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".