Who cares about health inequalities? Cross-country evidence from the World Health Survey
Bibliographic record
Abstract
Reduction of health inequalities within and between countries is a global health priority, but little is known about the determinants of popular support for this goal. We used data from the World Health Survey to assess individual preferences for prioritizing reductions in health and health care inequalities. We used descriptive tables and regression analysis to study the determinants of preferences for reducing health inequalities as the primary health system goal. Determinants included individual socio-demographic characteristics (age, sex, urban residence, education, marital status, household income, self-rated health, health care use, satisfaction with health care system) and country-level characteristics [gross domestic product (GDP) per capita, disability-free life expectancy, equality in child mortality, income inequality, health and public health expenditures]. We used logistic regression to assess the likelihood that individuals ranked minimizing inequalities first, and rank-ordered logistic regression to compare the ranking of other priorities against minimizing health inequalities. Individuals tended to prioritize health system goals related to overall improvement (improving population health and health care responsiveness) over those related to equality and fairness (minimizing inequalities in health and responsiveness, and promoting fairness of financial contribution). Individuals in countries with higher GDP per capita, life expectancy, and equality in child mortality were more likely to prioritize minimizing health 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 | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".