Leaving Sweden behind: Gains in life expectancy in Canada
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".