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Record W7097408166

Dorling D. Geographical inequalities of mortality by income in two developed island countries: a cross-national comparison of Britain and Japan. Soc Sci Med 2005;60:2865–75

2014· article· en· W7097408166 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsInequalityMerge (version control)Economic inequalityDistribution (mathematics)Income distributionMortality ratePopulation
DOInot available

Abstract

fetched live from OpenAlex

In this paper we examine the ecological relations between household income distribution and age-grouped mortality in Britain and Japan. Comparable datasets were prepared in terms of age intervals of mortality, household income intervals and geographical units for years around 1990. Then we conducted series of regression analyses to associate absolute and relative income indices with age and sex-specific standardized mortality ratios (SMRs). The results are as follows: (1) In Britain mortality is lower where inequalities in income are lower, while in Japan there is no obvious relationship. It is, however, apparent that- just as in the case of the USA and Canada- Britain and Japan appear to merge and appear part of greater pattern when considered as series of city regions. Thus an overall global relationship between income inequality and mortality may exist. To assess such global relationship, further studies using cross-national regional datasets covering wide rage of rich nations are desirable. (2) Income–mortality relations are consistent among different age–sex groups in Britain, but there are substantial differences in the relationships as revealed between different demographic groups in Japan. In particular, while absolute income levels are correlated negatively with mortality of working-age men in both countries, mortality of elderly people in Japan is higher where absolute income is higher. This indicates the different historical contexts to the health divides these two different geographical contexts, but further consideration of more historically nuanced understanding of income–mortality relations is required.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.432
Teacher spread0.383 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

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