Funding Long-Term Care in Canada: Who is Responsible for What?
Bibliographic record
Abstract
As Adams and Vanin (2016) have noted, different ways of funding long-term care (LTC) have different implications. Because health is not just healthcare, and LTC is not homogeneous, determining the appropriate public-private mix is complex. We suggest that how issues are framed helps influence policy choices, including who should pay for what, and how things should be financed. In addition, the distribution of expenditures for some services can be highly skewed, affecting the extent to which average cost data are useful in extrapolating their costs. We note that health expenditures fall into multiple categories, each presenting different policy issues. For example, framing LTC as health, as basic costs associated with living or as forced savings (like pensions) affects which funding approaches might be used, and the extent to which changes in the population distribution will affect cost structures. Underlying these discussions are questions of solidarity, and how much we believe that we are our brother's - or grandmother's - keeper.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.043 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".