Infant Neurodevelopment is Affected by Prenatal Maternal Stress: The <scp>QF</scp>2011 Queensland Flood Study
Bibliographic record
Abstract
Research shows that prenatal maternal stress (PNMS) negatively affects a range of infant outcomes; yet no single study has explored the effects of stress in pregnancy from a natural disaster on multiple aspects of infant neurodevelopment. This study examined the effects of flood-related stress in pregnancy on 6-month-olds' neurodevelopment and examined the moderating effects of timing of the stressor in gestation and infant sex on these outcomes. Women exposed to the 2011 Queensland (Australia) floods in pregnancy completed surveys on their flood-related objective and subjective experiences at recruitment and reported on their infants' neurodevelopment on the problem solving, communication, and personal-social scales of the Ages and Stages-III at 6 months postpartum (N = 115). Interaction results showed that subjective flood stress in pregnancy had significantly different effects in boys and girls, and that at high levels of stress girls had significantly lower problem solving scores than boys. Timing of the flood later in pregnancy predicted lower personal-social scores in the sample, and there was a trend (p < .10) for greater objective flood exposure to predict lower scores. PNMS had no effect on infants' communication skills. In conclusion, differential aspects of maternal flood-related stress in pregnancy influenced aspects of 6-month-olds' neurodevelopment.
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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 | 0.001 |
| 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 | 0.000 | 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".