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Record W2995820884 · doi:10.1177/2516103219892276

Maternal adverse childhood experiences, current cumulative risk, and behavioral dysregulation among child welfare involved children

2019· article· en· W2995820884 on OpenAlexaff
Brett Greenfield, Abigail Williams‐Butler, Kathleen Pirozzolo Fay, Jacquelynn F. Duron, Emily Adlin Bosk, Kate Stepleton, Michael J. MacKenzie

Bibliographic record

VenueDevelopmental Child Welfare · 2019
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMcGill University
FundersState of New Jersey Department of Children and Families
KeywordsCumulative riskPsychologyDevelopmental psychologyAdverse Childhood ExperiencesWelfareCumulative effectsFragile Families and Child Wellbeing StudyMedicinePsychiatryMental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.011
GPT teacher head0.260
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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