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Record W2015316893 · doi:10.2471/blt.11.097378

International shortfall inequality in life expectancy in women and in men, 1950-2010

2012· article· en· W2015316893 on OpenAlexaff
Ahmad Reza Hosseinpoor, Sam Harper, Jennifer Lee, John Lynch, Colin Mathers, Carla AbouZahr

Bibliographic record

VenueBulletin of the World Health Organization · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsLife expectancyInequalityPopulationDemographyEconomic inequalityDemographic economicsEconomicsSociologyMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess international shortfall inequality in life expectancy at birth among women and men and the influence of geography and country income group. METHODS: The authors used estimates of life expectancy at birth, by sex, for 12 five-year periods between 1950-1955 and 2005-2010 and estimates of population for the midpoints of each period from the World population prospects, 2008 revision. Shortfall inequality was defined as the weighted average of the deviations of each country's average life expectancy by sex from the highest attained life expectancy by sex for each period. FINDINGS: International shortfall inequalities in life expectancy among men and among women decreased between 1950 and 1975 but stagnated thereafter. International shortfall inequality in life expectancy has been higher in women than in men, ranging from 1.9 to 2.9 years. Women in low-income countries have the biggest shortfall, currently at around 26.7 years. CONCLUSION: International shortfall inequality is higher among women than men primarily because women in low-income and lower-middle-income country groups show larger differences in life expectancy than men. Further investigation is needed to determine the pathways causing these inequalities.

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)0.002
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 score0.187
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.321
Teacher spread0.299 · 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

Citations34
Published2012
Admission routes1
Has abstractyes

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