Fetal programming of infant temperament: An examination of prenatal maternal stress during the COVID‐19 pandemic
Bibliographic record
Abstract
Pregnant women were exposed to multiple sources of stress during the COVID-19 pandemic, raising concerns about the potential fetal programming effects of child development. A few studies show that prenatal maternal stress during the pandemic is associated with greater negative affectivity and more extraversion in infants. However, studies investigating this association are very few and need to be replicated. This study aims to prospectively investigate the association between prenatal maternal stress during the COVID-19 pandemic and infant temperament, while assessing the relative contribution of postnatal maternal stress. A total of 269 low-risk, French-speaking women from the province of Quebec, Canada, completed questionnaires during pregnancy to report on their prenatal maternal stress (general and pandemic-related). When their child was 6 months old, the mothers completed a second questionnaire to collect information on postnatal stress and infant temperament. The results show that prenatal maternal stress in the context of the pandemic significantly predicted infants' negative affectivity and orienting/regulation factors, even after controlling for postnatal stress. The results support the fetal programming hypothesis, while highlighting the additional contribution of maternal stress during the child's first months of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".