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Record W2260812587

Mortality rates among children and teenagers living in Inuit Nunangat, 1994 to 2008.

2012· article· en· W2260812587 on OpenAlexaffabout
Lisa Oliver, Paul A. Peters, Dafna Kohen

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCensusDemographyPopulationMortality rateGeographyHealth statisticsMedicineSociology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Because Vital Statistics data do not include information on Inuit identity in all jurisdictions, mortality rates cannot be calculated specifically for Inuit. However, Inuit in Canada are geographically concentrated--78% live in Inuit Nunangat, and 82% of the area's total population identify as Inuit. While there are limitations, geographic approaches can be employed to calculate mortality for the population of that area. DATA AND METHODS: The Vital Statistics Database (1994 to 2008) and population estimates were used to calculate age-standardized mortality rates (ASMRs) in five-year intervals around the 1996 and 2006 Census years. Mortality rates were calculated for 1- to 19-year-olds living in Inuit Nunangat and those living elsewhere in Canada. RESULTS: The ASMR in 2004-2008 for 1- to 19-year-olds in Inuit Nunangat was 188.0 deaths per 100,000 person-years at risk, five times the rate (35.3) elsewhere in Canada. The disparity had not narrowed over the previous decade. In Inuit Nunangat, injuries were responsible for 64% of deaths of children and teenagers, compared with 36% in the rest of Canada. INTERPRETATION: The persistently high mortality rates for children and teenagers living in Inuit Nunangat, compared with the rest of Canada, are important in understanding the health and socio-economic situation of residents of this region.

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)0.000
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 score0.137
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.331
Teacher spread0.295 · 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

Citations40
Published2012
Admission routes2
Has abstractyes

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