Family-level intelligence and maternal health: A cross-cohort, cross-generational longitudinal study using the NLSY
Bibliographic record
Abstract
This study examines the association between family-level intelligence metrics, and maternal health outcomes in middle age, as captured in the National Longitudinal Survey of Youth. Building on past research documenting links between maternal intelligence and health, our study expands the inquiry by exploring how both variations and trends in family-level intelligence are associated with maternal middle-age health. We use multilevel modeling analysis to extract family intelligence levels and growth scores from children's Peabody Individual Achievement Test of math, reading recognition and reading comprehension. We use two time-points, ten years apart, to extract levels and growth scores from maternal middle-aged health data. We then use canonical correlation analysis to examine the associations between family intelligence and maternal health. Our results show a positive association between family cognition and maternal health. Families with greater math and reading recognition levels experience better levels of maternal health outcomes. Patterns also suggest that low levels in math and reading comprehension are related to larger declines in physical health. We discuss implications of intellectual development in the family, noting that higher family intelligence not only holds intrinsic value but also is associated with improved maternal health outcomes. We discuss a possible "Flynn effect transfer" within the family context, where intellectual advancement correlates with positive health trajectories in midlife mothers. Future research could extend these insights to explore further downstream effects on both maternal and child well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".