Bibliographic record
Abstract
Federal health care funding has long been a source of policy debate in this country, a situation exacerbated recently by the COVID-19 pandemic and the calls by premiers for a massive expansion of the Canada Health Transfer. In this paper, after briefly reviewing the evolution of federal health care funding in Canada since the 1950s, we formulate three potential policy pathways federal policymakers might consider in order to improve health care funding in the country. The first pathway is the status quo, which simply preserves the Canada Health Transfer (CHT) as is. Explaining what the status quo entails is important to gauge the potential impact of the two other pathways we formulate, which depart from the status quo in a significant manner: first, the implementation of demographic adjustments that add to CHT as populations age; and second, the creation of a joint federal-provincial-territorial taxation regime. While the second pathway would constitute a form incremental change, the third one would be transformative in nature and, therefore, more challenging to implement, which is not a reason to exclude it for consideration, especially if we take a more long-term view of potential policy change in fiscal federalism. These three potential pathways should allow policymakers to consider how to adapt to changing circumstances while addressing the concerns of citizens and the demands of provincial/territorial governments. We do not support one or another of these policy pathways; instead, we explain what they are and what impact they could have, leaving the reader decide what option they prefer.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".