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Record W4412789045 · doi:10.1037/tra0001992

Child maltreatment typologies within intergenerational contexts: A latent class study.

2025· article· en· W4412789045 on OpenAlexafffund
Audrey Kern, Thomas J. Esposito, Sonia Hélie, Rachel Langevin

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalMcGill University
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of Canada
KeywordsLatent class modelClass (philosophy)PsychologyDevelopmental psychologySociologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: Child maltreatment (CM) in one generation can predict CM in the next, a concept known as the intergenerational continuity of CM. However, the specific forms of intergenerational CM remain unclear, and fathers are largely absent from this body of literature. This study examined second-generation CM typologies in families where intergenerational continuity was present and assessed the impact of parental CM on these typologies using multilevel latent class analyses. METHOD: = 5,861 children). Two models were examined, one based on intergenerational CM on the mother's side and the other on the father's side. RESULTS: In both models, four latent classes were identified: (a) sexual abuse, physical abuse, and neglect, (b) risk of CM, (c) psychological abuse and exposure to intimate partner violence, and (d) high polyvictimization. Child age and maternal histories of sexual and physical abuse were associated with child class membership, while no paternal CM experiences were associated. CONCLUSIONS: These findings clarify the co-occurrence of CM types and how parental CM influences child typologies of CM in a two-generation sample, providing crucial insights for CM prevention. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.155
GPT teacher head0.508
Teacher spread0.353 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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