Dyadic flexibility and positive affect in parent–child coregulation and the development of child behavior problems
Bibliographic record
Abstract
Parent-child dyadic rigidity and negative affect contribute to children's higher levels of externalizing problems. The present longitudinal study examined whether the opposite constructs of dyadic flexibility and positive affect predicted lower levels of externalizing behavior problems across the early childhood period. Mother-child (N = 163) and father-child (n = 94) dyads engaged in a challenging block design task at home when children were 3 years old. Dynamic systems methods were used to derive dyadic positive affect and three indicators of dyadic flexibility (range, dispersion, and transitions) from observational coding. We hypothesized that the interaction between dyadic flexibility and positive affect would predict lower levels of externalizing problems at age 5.5 years as rated by mothers and teachers, controlling for stability in externalizing problems, task time, child gender, and the child's effortful control. The hypothesis was supported in predicting teacher ratings of child externalizing from both mother-child and father-child interactions. There were also differential main effects for mothers and fathers: mother-child flexibility was detrimental and father-child flexibility was beneficial for child outcomes. Results support the inclusion of adaptive and dynamic parent-child coregulation processes in the study of children's early disruptive behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".