Disparities in Disability by Educational Attainment Across US States
Bibliographic record
Abstract
OBJECTIVES: To examine how disparities in adult disability by educational attainment vary across US states. METHODS: We used the nationally representative data of more than 6 million adults aged 45 to 89 years in the 2010-2014 American Community Survey. We defined disability as difficulty with activities of daily living. We categorized education as low (less than high school), mid (high school or some college), or high (bachelor's or higher). We estimated age-standardized disability prevalence by educational attainment and state. We assessed whether the variation in disability across states occurs primarily among low-educated adults and whether it reflects the socioeconomic resources of low-educated adults and their surrounding contexts. RESULTS: Disparities in disability by education vary markedly across states-from a 20 percentage point disparity in Massachusetts to a 12-point disparity in Wyoming. Disparities vary across states mainly because the prevalence of disability among low-educated adults varies across states. Personal and contextual socioeconomic resources of low-educated adults account for 29% of the variation. CONCLUSIONS: Efforts to reduce disparities in disability by education should consider state and local strategies that reduce poverty among low-educated adults and their surrounding contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".