Homotypical and Heterotypical Intergenerational Continuity of Child Maltreatment: Evidence from a Cohort of Families Involved with Child Protection Services
Bibliographic record
Abstract
Child maltreatment (CM) in one generation can predict CM in the next generation, a concept known as intergenerational continuity. Yet, the form taken by the intergenerational continuity of CM remains unclear and fathers are mostly absent from this literature. This longitudinal study aimed to document patterns of intergenerational continuity of substantiated CM, on the maternal and paternal sides, by examining the presence of: homotypical CM, which is the same type of CM in both generations; and heterotypical CM, which is different CM types in both generations. The study included all children substantiated for CM with the Centre Jeunesse de Montréal between 1 January 2003, and 31 December 2020, with at least one parent who was also reported to that agency during their childhood (n = 5861 children). The cohort was extracted using clinical administrative data, and logistic regression models were tested with the children’s CM types as the dependent variables. Homotypical continuity was found for: (1) physical abuse on the paternal side; (2) sexual abuse on the maternal side; and (3) exposure to domestic violence on the maternal side. Heterotypical continuity was also prevalent, but to a lesser extent. Interventions helping maltreated parents overcome their traumatic past are essential to foster intergenerational resilience.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".