Can We Avoid a Sick Fiscal Future? The Non-Sustainability of Health-Care Spending with an Aging Population
Bibliographic record
Abstract
Funding for Canadian public health care has long relied on a “pay-as-you-go” funding model: for the most part, government pays for health costs each year from taxes collected in that fiscal year with effectively nothing put aside for projected rising health-care costs in the future. But the future of Canadian public health care is going to get more expensive as the relatively large cohort of baby boomers reaches retirement age. As they exit the work force, and enter the ages at which Canadians use the health-care system more, a smaller population of younger workers is going to be left paying the growing health-care costs of older Canadians. If Canadians intend to preserve a publicly funded medicare system that offers a similar level of service in the future as it does today, under the pay-as-you-go model, eventually peak taxes for Canadians born after 1988 will end up twice as high as the peak taxes that the oldest baby boomers paid. The “payas-you-go” model has become like a Ponzi scheme, where those who got in early enough make out nicely, while those who arrive late stand to suffer a serious financial blow. This should concern both Canadians who value a comprehensive public health system as well as Canadians who value competitive tax rates: There is no reason to be certain that future taxpayers will blithely accept having their taxes substantially increased to finance health care for another, older generation that did not pay for a significant portion of its own health care. If the burden proves too high for the taxpaying public to accept, that could well jeopardize Canada’s health-care system as we know it. If Canadians intend to preserve their iconic public health system, and are unprepared to unjustly overburden future generations with the tax bill left by their parents and grandparents, provincial governments must make strong and rapid efforts to reform the health system. They must find more cost-efficient ways of managing medicine, including new approaches to eldercare, chronic disease prevention and better health promotion. If policymakers respond in time with a workable strategy and adequate effort, the substantial financial health-care liability currently faced by future generations may not be eliminated entirely, but it can still be reduced dramatically.
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 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| 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".