Developmental origins of infant stress reactivity profiles: A multi‐system approach
Bibliographic record
Abstract
BACKGROUND: This study tested the hypothesis that maternal physiological and psychological variables during pregnancy discriminate between theoretically informed infant stress reactivity profiles. METHODS: The sample comprised 254 women and their infants. Maternal mood, salivary cortisol, respiratory sinus arrhythmia (RSA), and salivary α-amylase (sAA) were assessed at 15 and 32 weeks gestational age. Infant salivary cortisol, RSA, and sAA reactivity were assessed in response to a structured laboratory frustration task at 6 months of age. Infant responses were used to classify them into stress reactivity profiles using three different classification schemes: hypothalamic-pituitary-adrenal (HPA)-axis, autonomic, and multi-system. Discriminant function analyses evaluated the prenatal variables that best discriminated infant reactivity profiles within each classification scheme. RESULTS: Maternal stress biomarkers, along with self-reported psychological distress during pregnancy, discriminated between infant stress reactivity profiles. CONCLUSIONS: These results suggest that maternal psychological and physiological states during pregnancy have broad effects on the development of the infant stress response systems. © 2016 Wiley Periodicals, Inc. Dev Psychobiol 58: 578-599, 2016.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".