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Record W3095696470 · doi:10.7202/1072588ar

A Conceptual Model of the Intergenerational Transmission of Emotion Dysregulation in Mothers with a History of Childhood Maltreatment

2020· article· en· W3095696470 on OpenAlexafffundvenue
Sarah Cabecinha‐Alati, Rachel Langevin, Tina Montreuil

Bibliographic record

VenueInternational Journal of Child and Adolescent Resilience · 2020
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMcGill University
FundersMcGill University
KeywordsSocializationPsychologyDevelopmental psychologyNarrativePsychological resilienceSocial psychology

Abstract

fetched live from OpenAlex

Objectives: Adults with a history of childhood maltreatment report problems with emotion regulation (ER) and parenting, which can contribute to maladaptive outcomes in offspring. The following narrative review consists of a theoretical and empirical synthesis of the literature examining child maltreatment, emotion regulation, and parenting, with an emphasis on parental emotion socialization. Method: Building upon the literature contained in the review, we developed a novel conceptual model that elucidates some of the mechanisms involved in the intergenerational transmission of emotion dysregulation among mothers with a history of childhood maltreatment. Taking into account risk and protective factors (e.g., socio-economic status, polyvictimization, teenage motherhood, access to social supports), our conceptual model highlights both direct (e.g., social learning) and indirect (e.g., ER difficulties) mechanisms through which child maltreatment contributes to problems with parental emotion socialization and ER difficulties in the next generation. Implications: Directions for future research and implications for intervention will be discussed with an emphasis on preventing the continuity of maladaptive parenting by promoting the development of parents’ ER abilities in a trauma-informed, resilience-focused framework.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2020
Admission routes3
Has abstractyes

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