MORTALITY, MORBIDITY, AND THE CHANGING NATURE OF THE ELDERLY POPULATION
Bibliographic record
Abstract
Throughout the 20th century, Canada as well as other developed countries experienced a tremendous transformation of their age structure, due to reduced fertility and mortality rates and changing causes of death. The first part of the century was characterized by the regression of mortality by infectious diseases, which helped nearly every child survive into adulthood, whereas the decades leading into the 21st century saw great improvements in mortality by cardiovascular diseases, which mainly profited older age groups. This greater survival at all ages helped raise Canadians ’ life expectancy from 56 to 77 years for men and from 58 to 82 years for women, between 1921 and 2000 (CHMD, 2005). Although the rate at which mortality declines has slowed down compared with the earlier part of the 20th century, life expectancy has never stopped increasing. This decline of mortality has lead to a significant increase of individuals surviving to age 65, as well as to age 80 and up. Along with the decrease in fertility, the decline in mortality contributes to the aging of the Canadian population: people aged 65 and over currently make up 13 percent of the country’s population compared with 8 percent in 1971. In addition, an aging process is developing within older age groups: the importance of the 80+ age group among the 65+ has increased
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".