Using Natural Disasters to Study the Effects of Prenatal Maternal Stress on Child Health and Development
Bibliographic record
Abstract
Research on the developmental origins of health and disease highlights the plasticity of the human fetus to a host of potential teratogens. Experimental research on laboratory animals has demonstrated a variety of physical and behavioral effects among offspring exposed to prenatal maternal stress (PNMS). However, these studies cannot elucidate the relative effects of the objective stress exposure and the subjective distress in a way that would parallel the stress experience in humans. PNMS research with humans is also limited because there are ethical challenges to designing studies that involve the random assignment of pregnant women to varying levels of independent stressors. Natural disasters present opportunities for natural experiments of the effects of pregnant women's exposure to stress on child development. In this review, we present an overview of the human and animal research on PNMS, and highlight the results of Project Ice Storm which has been following the cognitive, behavioral, motor and physical development of children exposed in utero to the January 1998 Quebec Ice Storm. We have found that both objective degree of exposure to the storm and the mothers' subjective distress have strong and persistent effects on child development, and that these effects are often moderated by the timing of the ice storm in pregnancy and by the child's sex.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".