Educational Inequalities in Health Behaviors at Midlife
Bibliographic record
Abstract
Education is a fundamental cause of social inequalities in health because it influences the distribution of resources, including money, knowledge, power, prestige, and beneficial social connections, that can be used in situ to influence health. Recent studies have highlighted early-life cognition as commonly indicating the propensity for educational attainment and determining health and age of mortality. Health behaviors provide a plausible mechanism linking both education and cognition to later-life health and mortality. We examine the role of education and cognition in predicting smoking, heavy drinking, and physical inactivity at midlife using data from the Wisconsin Longitudinal Study (N = 10,317), National Survey of Health and Development (N = 5,362), and National Childhood Development Study (N = 16,782). Adolescent cognition was associated with education but was inconsistently associated with health behaviors. Education, however, was robustly associated with improved health behaviors after adjusting for cognition. Analyses highlight structural inequalities over individual capabilities when studying health behaviors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".