The Effects of Population Ageing on the Canadian Health Care System
Bibliographic record
Abstract
There is probably no policy-maker in Canada who has not heard "the boom, bust and echo" mantra of David Foot (1996) by now. Even those who have not fallen prey to Foot's mantra are aware that between 2025 and 2031, the population aged 65 and over will reach between 20 and 25 percent of the total Canadian population. While the timing of this trend is somewhat later for Canada than it is for some northern and western European countries, policy makers in Canada and in many other countries of the Organisation for Economic Co-operation and Development (OECD) are receiving conflicting messages about what the future growth of the elderly population will mean for the provision of health care services and health care expenditures. There are those who believe that because seniors account for a disproportionate part of health expenditures relative to their proportion of the population, that as this proportion grows health care expenditures will either explode or the health care system will have to be reconstructed in ways which are incompatible with the current values of health care systems in Canada as defined by the Canada Health Act (i.e., public administration, comprehensiveness, universality, portability, and accessibility). Alternatively, there are those who believe that the growth in the seniors population is only one component which is driving costs and that those components are manageable. In this paper, the relationship between population ageing and future health care costs is assessed based on evidence from the Canadian and international literature on this topic. The contributing factors to health care system costs and how they interface with an ageing population are identified. The paper also assesses where new research is needed if the publicly-financed health care system is to evolve to respond to the needs of an ageing population in a fiscally and socially responsible manner.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".