The Cascade Effect of Parent Dysfunction: An Emotion Socialization Transmission Framework
Bibliographic record
Abstract
The current study tested a preliminary cascade model of parent dysfunction—i.e., internalizing psychopathology and emotion dysregulation—whereby parent dysfunction is transmitted to children through the impact of parental emotion socialization on child emotion regulation. Participants were 705 mothers (Mage = 36.17, SD = 7.55) and fathers (Mage = 35.43, SD = 6.49) of children aged 8 to 12 years who self-reported on their internalizing psychopathology, emotion regulation difficulties, and emotion socialization practices, and on their child’s internalizing psychopathology and emotion regulation. Using a split sample method, we employed a data-driven approach to develop a conceptual model from our initially proposed theoretical model with the first subsample (n = 352, 51% mothers), and then validated this model in a second subsample (n = 353, 49% mothers). Results supported a model in which the transmission of dysfunction from parent to child was sequentially mediated by unsupportive parental emotion socialization—but not supportive parental emotion socialization—and child emotion dysregulation. The indirect effects from the final model did not differ by parent gender. Findings provide preliminary support for a mechanism by which maternal and paternal internalizing psychopathology and emotion dysregulation disrupt parental emotion socialization by increasing unsupportive emotion socialization practices, which impacts children’s development of emotion regulation skills and risk for internalizing psychopathology.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".