Revue de la littérature sur l'évolution future de l'espérance de vie et de l'espérance de vie en santé
Bibliographic record
Abstract
Like many industrialized countries, Canada is experiencing significant population aging and this phenomenon, inherited from the demographic transition, will intensify in the coming years. Mortality changes, especially at older ages, will contribute greatly to this phenomenon, hence the importance to be aware of the latest and forthcoming developments. It is also imperative to uncover recent and future health trends in the elderly population, and to investigate whether extra years of life gained through increased longevity will be spent in good or bad health. Thus, through this literature review, we first outline the academic debate on the future of mortality, and more specifically of life expectancy at birth. Since the debate essentially crystallized around two main competing views, one that supports sustained mortality gains in the future and one that expect instead these gains to peak, the arguments of each group and the main criticisms they face are exposed. We then provide a detailed account of a concomitant debate on the quality rather than the quantity of years lived. The three competing theories on the future of morbidity - compression of morbidity, expansion of morbidity and dynamic equilibrium - are presented and their relevance is discussed on the basis of empirical data. The difficulties inherent in defining the concepts of health and illness, and to obtain comparable indicators over time and space are highlighted.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".