Bibliographic record
Abstract
There is a large body of evidence supporting the notion that maternal factors, such as age and mental health, affect her child’s development. In contrast, much less is known about the paternal contribution to child outcomes, though preliminary investigations have identified specific developmental risks linked to extremes of paternal age, such as lower IQ and/or behavioral problems. Hence, fathers, like mothers, influence their child’s development, whether through prenatal biological alterations in the father’s sperm cells, postnatal variations in the household and learning environment, and/or quality of the father-child or couple relationships. Any developmental risks linked to paternal age may be amplified during middle childhood (6-8 years old) as children graduate from kindergarten and adjust to the more demanding environment of elementary school. This developmental stage also coincides with a unique endocrine event shown to influence brain development, adrenarche. As such, this project aims to investigate whether the child’s androgen and cortisol production during adrenarche moderates the relationships between father’s age (alone or in relation to mother’s age) and the child’s cognition and behavior during the school transition. Data from a sub-cohort of the 3D study (n=61) was collected on parents and children from the 1st trimester of pregnancy to 6-8 years postpartum. We found extremes of paternal age and parental age gaps to interact with the child’s androgen to cortisol ratio in moderating behavioral risk, especially measurable differences in externalizing symptoms such as conduct or inattention/hyperactivity symptoms. Based on these findings, we propose a new conceptual model for father-child risk transmission based on the level of “fitness” between a father’s age and his child’s hormonal profile and discuss future implications of this model in research and clinical practice
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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 | 0.002 |
| 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 | 0.001 | 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".