CHILDHOOD SOCIOECONOMIC STATUS GRADIENTS IN FUNCTIONAL LIMITATIONS: THE ROLE OF MACRO-ECONOMIC CONTEXT
Bibliographic record
Abstract
Abstract A large number of studies have documented an association between childhood socioeconomic status (SES) and adult health or illness. However, relatively little is known about how this SES-health gradient varies across macro-economic contexts. The current study examined state-level mean income as a moderator of the association between the education level of a person’s parents and functional limitations in the United States (N = 10,685, Mean age = 47.7, SD = 13.6, 51% female, 86% White; 6% Black; 8% other or missing). Data were from an economic time series database (1917-2018) and the Midlife in the United States Study (MIDUS). Time series data were merged with MIDUS data (1995 core sample and 2012 refresher) by state of residence and year to determine exposure to state economic characteristics during three periods of the life span: prime working years (ages 30-55), early adulthood (20-25) and childhood (0-15). Results indicated that the SES-functional limitations gradient was attenuated for those who lived in a state with a higher mean income from ages 30-55, 20-25, and 0-15 (p’s < .05). The moderating role of state-level economic exposures between ages 30 and 55 (but not at earlier life stages) differed by gender such that the attenuation of the SES-functional limitations gradient in more affluent states was more strongly evident among females than males. Findings suggest that the role of childhood SES in later life health varies across state-level economic contexts with notable differences by gender and age group. Future directions and policy implications are discussed.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".