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Record W4220743465 · doi:10.1177/10775595221081795

Maternal History of Child Maltreatment and Household Chaos: Examining the Mediating Role of Maternal and Child Psychopathology

2022· article· en· W4220743465 on OpenAlexafffund
Krysta Andrews, Jennifer E. Khoury, Ashwini Tiwari, Sawmmiya Kirupaharan, Andrea González

Bibliographic record

VenueChild Maltreatment · 2022
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council
KeywordsPsychopathologyPoison controlPsychological interventionInjury preventionPsychologyMental healthSuicide preventionStructural equation modelingChild abuseChild psychopathologyHuman factors and ergonomicsClinical psychologyDevelopmental psychologyOccupational safety and healthOffspringPsychiatryMedicinePregnancyEnvironmental health

Abstract

fetched live from OpenAlex

Caregiver history of childhood maltreatment can have pervasive effects on familial and household dynamics. Maternal history of child maltreatment (MCM) is linked to maternal depressive symptoms and offspring behavioural problems. Further, maternal and child mental health are associated with chaotic home environments. In this study, we examined the potential mediating roles of maternal depressive symptoms and child behavioural problems in the association between MCM and household chaos. A sample of 133 mother-child dyads participated in home visits during which mothers completed questionnaires measuring their history of child maltreatment, depressive symptoms, household chaos and child behaviour problems. Mothers also conducted videotaped home tours related to household chaos. Structural equation modelling results indicated that MCM was indirectly associated with higher household chaos via elevated maternal depressive symptoms and child externalizing, but not internalizing behaviour problems. Interventions aimed at mitigating the effects of MCM on maternal and child psychopathology may positively influence household dynamics.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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