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Record W2109353468 · doi:10.1177/1403494815577459

Leaving Sweden behind: Gains in life expectancy in Canada

2015· article· en· W2109353468 on OpenAlexaffabout
Nathalie Auger, Emilie Le Serbon, Mikael Rostila

Bibliographic record

VenueScandinavian Journal of Public Health · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsUniversité de MontréalInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsLife expectancyDemographyPopulationPopulation ageingGerontologyMedicineAgeingGeographySociology

Abstract

fetched live from OpenAlex

AIMS: Sweden and Canada are known for quality of living and exceedingly high life expectancy, but recent data on how these countries compare are lacking. We measured life expectancy in Canada and Sweden during the past decade, and identified factors responsible for changes over time. METHODS: We calculated life expectancy at birth for Canada and Sweden annually from 2000 to 2010, and determined the ages and causes of death responsible for the gap between the two countries using Arriaga's method. We determined how population growth, ageing, and mortality influenced the number of deaths over time. RESULTS: During 2000-2010, life expectancy in Canada caught up with Sweden for men, and surpassed Sweden by 0.4 years for women. Sweden lost ground owing to a slower reduction in circulatory and tumour mortality after age 65 years compared with Canada. Nonetheless, population ageing increased the number of deaths in Canada, especially for mental and nervous system disorders. In Sweden, the number of deaths decreased. CONCLUSIONS: In only one decade, life expectancy in Canada caught up and surpassed Sweden due to rapid improvements in circulatory and tumour mortality. Population ageing increased the number of deaths in Canada, potentially stressing the health care system more than in Sweden.

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 score0.077
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.164
GPT teacher head0.375
Teacher spread0.211 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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