Emotion regulation from infancy to toddlerhood: Individual and group trajectories of full‐term and very‐low‐birthweight preterm infants
Bibliographic record
Abstract
Adaptive emotion regulation begins with infants operating jointly with their parents to regulate their emotions, which fosters the development of independent regulation. Little is known about when or how this transition occurs, or the impact of factors such as parental availability or premature birth status. The current study examined the use of self-soothing, attentional distraction, and dyadic regulation in full-term and healthy very-low-birthweight (VLBW) preterm infant-mother dyads at 5 ½, 12, and 18 months of age. At 5 ½ months, dyads participated in the Still-Face procedure. At 12 and 18 months, dyads participated in two free-play interactions, a puzzle task, and an interference task. Emotion regulation behaviors were coded using two systematic, observational systems. Results indicated that infants used less self-soothing and attentional distraction and more dyadic regulation as they aged. Increased use of self-soothing at earlier ages predicted increased use of dyadic regulation at subsequent ages. Toddlers used more independent, attention-seeking, and escape behavior during periods of maternal unavailability. There were no significant differences between full-term and VLBW/preterm toddlers' emotion regulation behaviors. Results from the current study contribute to the understanding of normative development of emotion regulation and the risk associated with prematurity.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".