Physical Functioning Trends among US Women and Men Age 45–64 by Education Level
Bibliographic record
Abstract
Functional limitations and disability declined in the US during the 1980s and 1990s, but reports of early 21st century trends are mixed. Whether educational inequalities in functioning increased or decreased is also poorly understood. Given the importance of disability for productivity, independent living, and health care costs, these trends are critical to US social and health policies. We examine recent trends in functional limitations and disability among women and men aged 45-64. Using 2000-2015 National Health Interview Surveys data on over 155,000 respondents, semiparametric and logistic regression models visualize and test functioning trends by education. Among women and men with at least a college degree, there was no change in disability and mild increase in limitations over time. All other education levels experienced significant increases in functioning problems ranging from 18% higher odds of functional limitations in 2015 compared to 2000 among men with some college to about 80% increase in the odds of disability among women and men with less than high school education. The similar trends for both genders suggest common underlying causes, possibly including the worsening economic well-being of middle- and working-class families. The pervasive growth of functioning problems is a cause for concern that necessitates further scholarly investigation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".