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Record W2158039690 · doi:10.1093/pubmed/fdr078

Socio-demographic diversity and unexplained variation in death rates among the most deprived parliamentary constituencies in Britain

2011· article· en· W2158039690 on OpenAlexaff
Helena Tunstall, Richard Mitchell, Julia Gibbs, Stephen Platt, Danny Dorling

Bibliographic record

VenueJournal of Public Health · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsEspace pour la vie
Fundersnot available
KeywordsDecileDemographyPopulationMortality rateEthnic groupGeographyMedicineStatisticsSociologyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: There is considerable unexplained variation in death rates between deprived areas of Britain. This analysis assesses the degree of variation in socio-demographic factors among deprivation deciles and how variables associated with deaths differ among the most deprived areas. METHODS: Death rates 1996-2001, Carstairs' 2001 deprivation score and indicators, population density, black and minority ethnic group (BME) and population change 1971-2001 were calculated for 641 parliamentary constituencies in Britain. Constituencies were grouped into Carstairs' deciles. We assessed standard errors of all variables by decile and the relationship between death rates and socio-demographic variables with Pearson's correlations and linear regression by decile and for all constituencies combined. RESULTS: Standard errors in death rates and most socio-demographic variables were greatest for the most deprived decile. Death rates among all constituencies were positively correlated with Carstairs' score and indicators, density and BME, but for the most deprived decile, there was no association with Carstairs and a negative correlation with overcrowding, density and BME. For the most deprived decile multivariate models containing population density, BME and change had substantially higher R(2). CONCLUSIONS: Understanding variations in death rates between deprived areas requires greater consideration of their socio-demographic diversity including their population density, ethnicity and migration.

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)0.009
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 score0.053
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.098
GPT teacher head0.320
Teacher spread0.222 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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