Association of rule of law and health outcomes: an ecological study
Bibliographic record
Abstract
OBJECTIVES: To explore whether the rule of law is a foundational determinant of health that underlies other socioeconomic, political and cultural factors that have been associated with health outcomes. SETTING: Global project. PARTICIPANTS: Data set of 96 countries, comprising 91% of the global population. PRIMARY AND SECONDARY OUTCOME MEASURES: The following health indicators, infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, were included to explore their association with the rule of law. We used a novel Rule of Law Index, gathered from survey sources, in a cross-sectional and ecological design. The Index is based on eight subindices: (1) Constraints on Government Powers; (2) Absence of Corruption; (3) Order and Security; (4) Fundamental Rights; (5) Open Government; (6) Regulatory Enforcement, (7) Civil Justice; and (8) Criminal Justice. RESULTS: The rule of law showed an independent association with infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, after adjusting for the countries' level of per capita income, their expenditures in health, their level of political and civil freedom, their Gini measure of inequality and women's status (p<0.05). Rule of law remained significant in all the multivariate models, and the following adjustment for potential confounders remained robust for at least one or more of the health outcomes across all eight subindices of the rule of law. Findings show that the higher the country's level of adherence to the rule of law, the better the health of the population. CONCLUSIONS: It is necessary to start considering the country's adherence to the rule of law as a foundational determinant of health. Health advocates should consider the improvement of rule of law as a tool to improve population health. Conversely, lack of progress in rule of law may constitute a structural barrier to health improvement.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".