Bibliographic record
Abstract
Income related health inequalities have been estimated for various groups of individuals at local, state, or national levels. Almost all of theses estimates are based on individual data from sample surveys. Lack of consistent individual data worldwide has prevented estimates of international income related health inequalities. This paper uses the (population weighted) aggregate data available from many countries around the world to estimate worldwide income related health inequalities. Since the intra-country inequalities are subdued by the aggregate nature of the data, the estimates would be those of the inter-country or international health inequalities. As well, the study estimates the contribution of major socioeconomic variables to the overall health inequalities. The findings of the study strongly support the existence of worldwide income related health inequalities that favor the higher income countries. Decompositions of health inequalities identify inequalities in both the level and distribution of income as the main source of health inequality along with inequalities in education and degree of urbanization as other contributing determinants. Since income related health inequalities are preventable, policies to reduce the income gaps between the poor and rich nations could greatly improve the health of hundreds of millions of people and promote global justice. Keywords: global, income, health inequality, socioeconomic determinants of health
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".