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Record W1993251563 · doi:10.3402/ijch.v63i0.17819

Mortality in the Kivalliq Region of Nunavut, 1987–1996

2004· article· en· W1993251563 on OpenAlexaffabout
Alexander Macaulay, Pamela Orr, Sharon Macdonald, Lawrence Elliott, Rosemary Brown, Anne Durcan, Bruce Martin

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsGovernment of NunavutUniversity of Manitoba
Fundersnot available
KeywordsCoronerMedicineMortality rateInfant mortalityDemographyCause of deathPopulationDiseaseSudden infant death syndromeInjury preventionPediatricsPoison controlEnvironmental healthSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: The Kivalliq region of Nunavut, Canada, had a 1996 population of 7,131, of which 87% were Inuit. An attempt was made to characterize patterns of mortality in the region. STUDY DESIGN: Descriptive regional mortality study, based on 10-year retrospective review of health records data. METHODS: All deaths and stillbirths of Kivalliq residents during the study period were identified. Available health records data were reviewed for each death, including medical charts, death certificates and coroner's reports where applicable. Age-standardized mortality rates, both overall and cause-specific, were calculated and compared to both Canadian national rates and territorial rates from the same time period. RESULTS: The infant mortality rate was 32.3/1,000 live births, five times Canada's rate. Leading causes of infant deaths were prematurity and Sudden Infant Death Syndrome (SIDS). The overall mortality rate was 1.8 times that of Canada, with leading causes of death being cancers (especially lung cancer), circulatory disease, respiratory disease, unintentional injury and suicide. CONCLUSIONS: Identified areas of concern included mortality due to premature birth, SIDS, unintentional injuries, suicides, respiratory disease and lung cancer. It is hoped that this study's results will assist territorial leaders, health workers and citizens in health planning activities.

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)0.003
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 score0.130
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.074
GPT teacher head0.437
Teacher spread0.363 · 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

Citations22
Published2004
Admission routes2
Has abstractyes

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