Postsecondary Education and Adult Health Lifestyles: Examining Sources of Disparities
Bibliographic record
Abstract
Objectives: Approximately 58.5 million Americans in their thirties and forties have some form of postsecondary education; however, health risk behaviors remain disproportionately concentrated among those with lower educational attainment. In this study, we examine and decompose disparities in adult health lifestyles by level of postsecondary education, identifying the underlying social and economic factors contributing to these differences. Methods: We drew data from Waves I (1995) and V (2018) of the National Longitudinal Study of Adolescent to Adult Health (Add Health). We analyzed a composite health behavior index, capturing binge drinking, smoking, marijuana use, physical activity, nutrition, and body mass index, using Poisson regression models and Kitagawa-Oaxaca-Blinder decomposition techniques. Explanatory factors include demographic characteristics, parental background, social context, economic well-being, and employment characteristics. Results: Economic well-being explained the largest share of the disparities in health behaviors by postsecondary education. Demographics, parental background, and social context also contributed significantly. However, after controlling for personal and household income, employment characteristics did not account for a notable portion of the observed behavioral disparities. Conclusions: Disparities in adult health lifestyles related to postsecondary education are mainly caused by structural socioeconomic factors. Addressing these underlying factors may be essential to reducing behavioral inequalities linked to educational attainment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".