An age- and cause decomposition of differences in life expectancy between residents of Inuit Nunangat and residents of the rest of Canada, 1989 to 2008.
Bibliographic record
Abstract
BACKGROUND: This study quantifies differences in life expectancy between residents of Inuit Nunangat and people in the rest of Canada; estimates the contribution of specific causes of death to the differences; and examines these differences over time, by sex and by age group. DATA AND METHODS: A geographic approach was used to decompose differences in life expectancy for residents of Inuit Nunangat, compared with people living outside this geographic area. Differences in life expectancy by cause, sex, and age group were calculated using the discrete method of decomposition and were applied to abridged life tables. Causes of death were classified according to Global Burden of Disease categories. Attributable causes of death were calculated for causes amenable to medical intervention and for smoking-related diseases. RESULTS: The largest contributor to life expectancy differences between males in Inuit Nunangat and the rest of Canada was injury, particularly self-inflicted injury at ages 15 to 24. For females, the largest contributors were malignant neoplasm and respiratory disease at ages 65 to 79. INTERPRETATION: The gap in life expectancy between residents of Inuit Nunangat and the rest of Canada can be attributed to specific groups of causes occurring within specific age ranges.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".