Emotion Regulation in At‐Risk Preschoolers: Longitudinal Associations and Influences of Maternal Histories of Risk
Bibliographic record
Abstract
Emotion regulation is a key challenge of early childhood. The present study examined emotion regulation behaviour longitudinally from infancy to preschool. The continuity of emotion regulation was explored within the larger ecological context of maternal childhood histories of aggression and social withdrawal and maternal use of constructive and non‐constructive behaviours. Forty‐five mothers with childhood histories of aggression or social withdrawal from the Concordia Longitudinal Risk Project, a prospective, longitudinal, intergenerational study, participated with their preschool children. Infants' emotion regulation behaviour was observed during interactions with their mothers when they were 5½ months of age. Emotion regulation in these same children as preschoolers was coded during an interference task using the Preschooler Self‐Regulatory Scheme. Longitudinal findings demonstrated continuity in children's use of emotion regulation behaviours from infancy to preschool. Higher maternal childhood histories of risk, specifically social withdrawal, contributed to the prediction of preschoolers' increased attention‐seeking behaviours. Mothers' use of non‐constructive verbalizations predicted more maladaptive styles of emotion regulation in their preschoolers. The findings highlight the importance of helping children develop adaptive emotion regulation skills from a young age and have implications for the design of preventive intervention programmes to help parents foster children's emotion regulation abilities. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".