A global comparative study of wealth-pain gradients: Investigating individual- and country-level associations
Bibliographic record
Abstract
Pain is a significant yet underappreciated dimension of population health. Its associations with individual- and country-level wealth are not well characterized using global data. We estimate both individual- and country-level wealth inequalities in pain in 51 countries by combining data from the World Health Organization's World Health Survey with country-level contextual data. Our research concentrates on three questions: 1) Are inequalities in pain by individual-level wealth observed in countries worldwide? 2) Does country-level wealth also relate to pain prevalence? 3) Can variations in pain reporting also be explained by country-level contextual factors, such as income inequality? Analytical steps include logistic regressions conducted for separate countries, and multilevel models with random wealth slopes and resultant predicted probabilities using a dataset that pools information across countries. Findings show individual-level wealth negatively predicts pain almost universally, but the association strength differs across countries. Country-level contextual factors do not explain away these associations. Pain is generally less prevalent in wealthier countries, but the exact nature of the association between country-level wealth and pain depends on the moderating influence of country-level income inequality, measured by the Gini index. The lower the income inequality, the more likely it is that poor countries experience the highest and rich countries the lowest prevalence of pain. In contrast, the higher the income inequality, the more nonlinear the association between country-level wealth and pain reporting such that the highest prevalence is seen in highly nonegalitarian middle-income countries. Our findings help to characterize the global distribution of pain and pain inequalities, and to identify national-level factors that shape pain inequalities.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".