A Conceptual Model of the Intergenerational Transmission of Emotion Dysregulation in Mothers with a History of Childhood Maltreatment
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".