Socioeconomic gradients in health in international and historical context
Bibliographic record
Abstract
This article places socioeconomic gradients in health into a broader international and historical context. The data we present supports the conclusion that current socioeconomic gradients in health within the United States are neither inevitable nor immutable. This literature reveals periods in the United States with substantially smaller gradients, and identifies many examples of other countries whose different social policy choices appear to have led to superior health levels and equity even with fewer aggregate resources. The article also sheds light on the potential importance of various hypothesized mechanisms in driving major shifts in U.S. population health patterns. While it is essential to carefully examine individual mechanisms contributing to health patterns, it is also illuminating to take a more holistic view of the set of factors changing in conjunction with major shifts in population health. In this article, we do so by focusing on the period of the 1980s, during which U.S. life expectancy gains slowed markedly relative to other developed countries, and U.S. health disparities substantially increased. A comparison with Canada suggests that exploring broad social policy differences, such as the weaker social safety net in the United States, may be a promising area for future investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".