Infant Emotion Regulation Strategy Moderates Relations between Self‐Reported Maternal Depressive Symptoms and Infant HPA Activity
Bibliographic record
Abstract
Children of mothers with depressive symptoms often have high cortisol levels. Research shows that various child characteristics (e.g., attachment pattern, internalizing behaviours, and temperament) moderate this association. We suggest that these characteristics share common variance with emotion regulation strategy. Therefore, we examine infant emotion regulation strategy as a moderator of the association between maternal depressive symptoms and infant hypothalamic–pituitary–adrenal (HPA) function. We hypothesize that infants who utilize more independent emotion regulation strategies and have mothers who report higher depressive symptoms will exhibit elevated cortisol levels. Participants were 193 mothers and infants (15 months old) recruited from the community. Self‐reported maternal depressive symptoms were assessed. Infant independent regulatory behaviours (withdrawal, wandering away, distraction, scanning, orienting to another object) were coded in the context of a Toy Frustration Task. Infant cortisol was collected via saliva samples at baseline, +20, and +40 minutes. Results indicate that infant emotion regulation strategy moderates the relation between mothers' self‐reported depressive symptoms and infant total cortisol output (AUCG) and cortisol reactivity (AUCI). Infants who employed more independent regulatory behaviours and have mothers with higher depressive symptoms experience greater cortisol secretion. We discuss the findings in relation to parent‐infant interactions and the adaptive nature of emotion regulation strategies, as they relate to HPA regulatory capacities. 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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".