Educational Disparities in Adult Health: U.S. States as Institutional Actors on the Association
Bibliographic record
Abstract
Despite numerous studies on educational disparities in U.S. adult health, explanations for the disparities and their growth over time remain incomplete. We argue that this knowledge gap partly reflects an individualist paradigm in U.S. studies of educational disparities in health. These studies have largely focused on proximal explanations (e.g., individual behaviors) to the neglect of contextual explanations (e.g., economic policies). We draw on contextual theories of health disparities to illustrate how U.S. states, as institutional actors, shape the importance of education for health. Using two nationally-representative datasets and seven health measures for adults aged 45-89, we show that the size of the educational gradient in health varies markedly across states. The size varies because of variation in the health of lower-educated adults. We use state excise taxes on cigarettes to illustrate one way that states shape educational disparities in health. Our findings underscore the necessity of contextualizing these disparities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".