Maternal adverse childhood experiences, current cumulative risk, and behavioral dysregulation among child welfare involved children
Bibliographic record
Abstract
The intergenerational influence of adverse childhood experiences on individual outcomes demonstrates a need for research that considers both personal and environmental contributors to risk. As such, the current study explored how maternal cumulative risks influence the relationship between maternal Adverse Childhood Experiences (ACEs) and their children’s behavioral dysregulation among families involved with the child welfare system ( N = 314). The importance of child age is also considered. The sample was stratified by age groups of children (1.5–5 years and 6–18 years), and the relationship between maternal ACEs, cumulative risk, and child behavior was assessed using OLS regressions. For younger children, maternal ACEs were only associated with externalizing behaviors when not controlling for cumulative risk, but cumulative risks were independently and significantly associated with both internalizing and externalizing behaviors. For older children, ACEs were independently associated with both types of behavior, but controlling for cumulative risk attenuated the strength of this relationship. Cumulative risks were also independently associated with older children’s internalizing and externalizing behaviors. Findings suggest the need to consider both individual and environmental risks for parents and children involved in the child welfare system, and the developmental timing and stability of that risk, in order to adequately support parent-child relationships as well as caregiving environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".