Mortality in the Kivalliq Region of Nunavut, 1987–1996
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".