Bibliographic record
Abstract
This working paper examines from a macroeconomic perspective the trajectory of total (public and private) healthcare spending in Canada over the next two decades. Our purpose is to estimate the extent to which healthcare spending is going to absorb a greater fraction of income than we have experienced to date, under two scenarios: a “baseline ” one calculated from parameters estimated from historical experience and an “optimistic ” one calculated from parameters which assume an unprecedented improvement in the efficiency and effectiveness of the healthcare system and large improvement in real potential output growth. In the base case, where total healthcare spending rises from nearly 12 percent to 18 percent of GDP over the two-decade period, governments would have to find revenue increases or expenditure reductions, or both, equivalent to about 4 percentage points of GDP if they continue to finance about 70 percent of total healthcare spending. Even in the optimistic case, they will have to find revenue increases or expenditure reductions of about 2 percentage points of GDP. Even if we in Canada are collectively incredibly successful in taking the difficult actions to improve the productivity, efficiency and effectiveness of the healthcare system (our optimistic case), we face difficult but necessary choices as to how both governments and individuals finance the rising costs of healthcare.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.346 | 0.245 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".