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Record W1948174310 · doi:10.25336/p6130b

Narrowing sex differences in life expectancy: regional variations, 1971-1991

2001· article· en· W1948174310 on OpenAlexafffundvenueabout
Frank Trovato, N. M. Lalu

Bibliographic record

VenueCanadian Studies in Population · 2001
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsLife expectancyDemographyContext (archaeology)GeographyPopulationSociology

Abstract

fetched live from OpenAlex

A number of industrialized nations have recently experienced some degrees of constriction in their long-standing sex differentials in life expectancy at birth. In this study we examine this phenomenon in the context of Canada’s regions between 1971 and 1991: Atlantic (Newfoundland, Nova Scotia, New Brunswick, Prince Edward Island); Quebec, Ontario, and the West (Manitoba, Saskatchewan, Alberta, British Columbia, Yukon and Northwest Territories). Decomposition analysis based on multiple decrement life tables is applied to address three questions: (1) Are there regional differentials in the degree of narrowing in the sex gap in life expectancy? (2) What is the relative contribution of major causes of death to observed sex differences in average length of life within and across regions? (3) How do the contributions of cause-of-death components vary across regions to either widen or narrow the sex gap in survival? It is shown that the magnitude of the sex gap is not uniform across the regions, though the differences are not large. The most important contributors to a narrowing of the sex gap in life expectancy are heart disease and external types of mortality (i.e., accidents, violence, and suicide), followed by lung cancer and other types of chronic conditions. In substantive terms these results indicate that over time men have been making sufficient gains in these causes of death as to narrow some of the gender gap in overall survival. Regions show similarity in these effects.

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)0.001
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 score0.480
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.231
GPT teacher head0.468
Teacher spread0.237 · 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

Citations13
Published2001
Admission routes4
Has abstractyes

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