Extending the income inequality hypothesis: Ecological results from the 2005 and 2009 Argentine National Risk Factor Surveys
Bibliographic record
Abstract
A consensus on income inequality as a social determinant of health is yet to be reached. In particular, we know little about the cross-sectional versus lagged effect of inequality and the robustness of the relationship to indicators that are sensitive to varying parts of the income spectrum. We test these issues with data from Argentina’s 2005 and 2009 National Risk Factor Surveys. Inequality was operationalised at the provincial level with the Gini coefficient and the Generalised Entropy (GE) index. Population health was defined as the age-standardised percentage of adults with poor/fair self-rated health by province. Our cross-sectional results indicate a significant relationship between inequality (Gini) and poor health ( r =0.58, p <0.01) in 2005. Using the GE index, a gradient pattern emerges in the correlation, and the r values increase as the index becomes sensitive to the top of the distribution. The relationship between 2005 inequality and 2009 health displays a similar pattern, but with generally smaller correlations than the 2005 cross-sectional results. Further advances in the income inequality and health literature require new theoretical models to account for how inequalities in different parts of the income spectrum may influence population health in different ways.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".