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Record W2168160838 · doi:10.2105/ajph.2009.189134

Income Inequality, Trust, and Population Health in 33 Countries

2010· article· en· W2168160838 on OpenAlexafffund
Frank J. Elgar

Bibliographic record

VenueAmerican Journal of Public Health · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsCarleton University
FundersCanadian Institutes of Health ResearchWorld Bank Group
KeywordsLife expectancyEconomic inequalityInequalityPopulation healthPopulationPublic healthHealth equityDemographic economicsSocial inequalityDemographyEconomicsEconomic growthSociologyMedicineHealth care

Abstract

fetched live from OpenAlex

OBJECTIVES: I examined the association between income inequality and population health and tested whether this association was mediated by interpersonal trust or public expenditures on health. METHODS: Individual data on trust were collected from 48 641 adults in 33 countries. These data were linked to country data on income inequality, public health expenditures, healthy life expectancy, and adult mortality. Regression analyses tested for statistical mediation of the association between income inequality and population health outcomes by country differences in trust and health expenditures. RESULTS: Income inequality correlated with country differences in trust (r = -0.51), health expenditures (r = -0.45), life expectancy (r = -0.74), and mortality (r = 0.55). Trust correlated with life expectancy (r = 0.48) and mortality (r = -0.47) and partly mediated their relations to income inequality. Health expenditures did not correlate with life expectancy and mortality, and health expenditures did not mediate links between inequality and health. CONCLUSIONS: Income inequality might contribute to short life expectancy and adult mortality in part because of societal differences in trust. Societies with low levels of trust may lack the capacity to create the kind of social supports and connections that promote health and successful aging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.405
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations152
Published2010
Admission routes2
Has abstractyes

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