Family-level intelligence and maternal health: A cross-cohort, cross-generational longitudinal study using the NLSY
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it