Prenatal maternal stress, fetal programming, and mechanisms underlying later psychopathology—A global perspective
Bibliographic record
Abstract
There is clear evidence that the mother's stress, anxiety, or depression during pregnancy can alter the development of her fetus and her child, with an increased risk for later psychopathology. We are starting to understand some of the underlying mechanisms including the role of the placenta, gene-environment interactions, epigenetics, and specific systems including the hypothalamic-pituitary-adrenal axis and cytokines. In this review we also consider how these effects may be different, and potentially exacerbated, in different parts of the world. There can be many reasons for elevated prenatal stress, as in communities at war. There may be raised pregnancy-specific anxiety with high levels of maternal and infant death. There can be raised interpersonal violence (in Afghanistan 90.2% of women thought that "wife beating" was justified compared with 2.0% in Argentina). There may be interactions with nutritional deficiencies or with extremes of temperature. Prenatal stress alters the microbiome, and this can differ in different countries. Genetic differences in different ethnic groups may make some more vulnerable or more resilient to the effects of prenatal stress on child neurodevelopment. Most research on these questions has been in predominantly Caucasian samples from high-income countries. It is now time to understand more about prenatal stress and psychopathology, and the role of both social and biological differences, in the rest of the world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".