Maternal mental health mediates the effect of prenatal stress on infant temperament: The Harvey Mom Study
Bibliographic record
Abstract
Abstract Prenatal maternal stress and mental health problems are known to increase risk for developmental psychopathology in offspring, yet pathways leading to risk or resiliency are poorly understood. In a quasi-experimental design, we prospectively examined associations between disaster-related prenatal stress, maternal mental health symptoms, and infant temperament outcomes. Mothers who were pregnant during Hurricane Harvey ( N = 527) reported on objective hardships (e.g., loss of belongings or income, evacuation, home flooding) related to the storm and subsequent mental health symptoms (anxiety/depression, posttraumatic stress) across time. At a postpartum assessment, mothers reported on their infant’s temperament (negative affect, positive affect, orienting/regulatory capacity). Greater objective hardship indirectly predicted higher levels of infant orienting/regulatory capacity through its association with increased maternal posttraumatic stress symptoms. Greater objective hardship also indirectly predicted higher levels of infant negative affect through its association with increased maternal anxiety/depression symptoms across time. Our findings suggest a psychological mechanism linking prenatal stress with specific temperamental characteristics via maternal mental health symptoms. Findings point to the importance of high-quality assessment and mental health services for vulnerable women and young children.
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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 | 0.001 | 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) | 0.003 | 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".