Learning to let go: Parental over-engagement predicts poorer self-regulation in kindergartners.
Bibliographic record
Abstract
Responsive parenting and parental scaffolding have been shown to foster executive functions (EFs) and self-regulation skills in young children, but could too much parental directive engagement be counterproductive? To answer this question, we examined parental responses when children were demonstrating active on-task behaviors in a community sample of 102 dyads. We measured the time that parents spend actively guiding children's behavior relative to following the child's lead and created a measure of parental over-engagement to index the degree of active parental engagement via positive control/scaffolding behaviors. We hypothesized that parental over-engagement would negatively relate to children's self-regulation and EF skills because it creates fewer opportunities for children to practice self-regulation by leading dyadic interaction with their parents. We used an innovative State-Space Grid method to capture second-to-second changes in parental and child behaviors during a set of structured tasks. We examined the conceptual overlap of over-engagement with the global ratings of parenting, revealing that parental over-engagement was negatively correlated with global ratings of parental scaffolding and unrelated to global ratings of parental sensitivity. Next, we showed that parental over-engagement predicted lower levels of child hot EFs and observed self-regulation, controlling for age, parent education, family income, and global ratings of parenting. The predictive validity of over-engagement was unique to times when the child was actively engaged and was absent when the child was passively engaged. This study contributes to the discussion of how parents can support the development of self-regulation during the transition to elementary school. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".