MATERNAL SELF-REPORTED DEPRESSIVE SYMPTOMS AND MATERNAL CORTISOL LEVELS INTERACT TO PREDICT INFANT CORTISOL LEVELS
Bibliographic record
Abstract
Three basic findings have emerged from research on maternal depressive symptoms and offspring hypothalamic-pituitary-adrenal functioning: (a) Mothers' depressive symptoms are positively associated with their offsprings' cortisol stress response, (b) numerous individual and interpersonal maternal characteristics moderate this association, and (c) maternal and infant cortisol levels are highly correlated. In combination, these findings have suggested that maternal cortisol levels may moderate the relation between maternal depressive symptoms and infant cortisol responsivity; the current study assessed this hypothesis. Participants were 297 mother-infant dyads who were recruited from the community. Maternal depressive symptoms were assessed via self-report. Dyads participated in two differentially stressful infant challenges when infants were 16 and 17 months old. Mother and infant salivary cortisol was collected before and after challenges. Results indicate that maternal cortisol levels moderated associations between maternal depressive symptoms and infant cortisol levels across both challenges. Infants showed higher cortisol levels if their mothers had both higher depressive symptoms and higher cortisol levels, as compared to infants of mothers with higher depressive symptoms and lower cortisol, and to infants of mothers with lower depressive symptoms and either higher or lower cortisol levels. We discuss findings in relation to environmental and biological factors that may contribute to the intergenerational transmission of depressive symptoms.
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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 | 0.004 |
| 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".