Impact of Aging Population on Healthcare Financing Needs in Canada
Bibliographic record
Abstract
With rapid aging of the population in Canada, healthcare policies need to evolve to ensure sustainability of a free and universal healthcare system in the country. As population ages, the need for more medical interventions and physician care results in an increase in cost of delivery in healthcare. While provinces and territories (P/T) are primarily responsible for financing and delivery of these services, the Federal Government plays a critical role in financing healthcare delivery through the Canada Health Transfer (CHT) fund. In this report, I use historical healthcare expenditure data and available projections for population growth to estimate the per capita healthcare costs for different P/T between 2022 and 2043. Results highlight that healthcare expenses in Canada are expected to rise significantly in coming years. I also show that the rate of growth and distribution of seniors demographics is not consistent between different regions in Canada, which means despite an overall increase in healthcare costs, the future funding needs to support the healthcare system will be unique for each region. Finally, I propose four recommendations based on the results with a focus on the role of Federal Government in financing healthcare system to meet P/T financing needs in coming years.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".