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Record W1957692436 · doi:10.1136/jech.56.6.434

Health inequalities in Britain: continuing increases up to the end of the 20th century

2002· article· en· W1957692436 on OpenAlexaboutno aff
George Davey Smith

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsInequalityMedicineResidenceQuarter (Canadian coin)DemographyGovernment (linguistics)Socioeconomic statusGovernment OfficeMortality rateDemographic economicsPopulationLocal governmentEnvironmental healthLawSociologyGeographyEconomicsPolitical science

Abstract

fetched live from OpenAlex

Socioeconomic inequalities in premature mortality in Britain increased over the second half of the 20th century, particularly from the early 1970s onwards.1 The magnitude of mortality differentials reflects the trend in income inequality, which has also undergone a dramatic increase over the past quarter century.1 The present British government have emphasised their commitment to reducing health inequalities. For example the Minister of Health, Alan Milburn, has stated that “Our ambition is to do something that no government—Tory or Labour—has ever done. Not only to improve the health of the nation, but also to improve the health of the worst off at a faster rate”.2 A set of targets for the reduction of health inequalities has been presented. To monitor progress in this regard we have produced updated analyses of premature mortality rates running through to the end of 1999. The mortality data are the Office for National Statistics digital records of all deaths in England and Wales, and equivalent records from the General Register Office (Scotland). The full postcode of the usual residence of the deceased was used to assign each death to the parliamentary constituency in which the deceased usually lived. The death data were provided for single years since 1990 and have been grouped into two year …

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)0.045
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.161
GPT teacher head0.429
Teacher spread0.269 · 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; both teacher heads agree on what is shown here.

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

Citations111
Published2002
Admission routes1
Has abstractyes

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