Maternal invalidation and child RSA reactivity to frustration interact to predict teacher-reported aggression among at-risk preschoolers
Bibliographic record
Abstract
BACKGROUND: Aggression is a transdiagnostic indicator of risk and represents one of the most common reasons children are referred for mental health treatment. Theory and research highlight the impact of maternal invalidation on child aggression and suggest that its influence may vary based on differences in child physiological reactivity. Moreover, the interaction between these risk factors may be particularly pronounced among children of mothers with emotion regulation (ER) difficulties. The current study examined the independent and interactive effects of maternal invalidation and child physiological reactivity to frustration on teacher-reported aggression in an at-risk sample of preschool children. METHOD: = 42.48 months; s.d. = 3.78; 56% female; 47% racial/ethnic minority). Groups of mothers with and without clinician-rated ER difficulties reported on maternal invalidation, and child respiratory sinus arrhythmia (RSA) was assessed continuously during a frustration task as an indicator of physiological reactivity. Teachers or daycare providers reported on child aggression. RESULTS: Results demonstrated positive associations between maternal ER difficulties and both maternal invalidation and child RSA reactivity to frustration. As expected, the interaction between maternal invalidation and child RSA reactivity was significant, such that higher maternal invalidation and greater child RSA reactivity to frustration predicted more aggression in a daycare or preschool setting. Importantly, this effect was demonstrated while controlling for demographic covariates and baseline RSA. CONCLUSIONS: Findings are in line with diathesis-stress and biosocial models of risk and point to multiple targets for prevention and early intervention.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".