Determining the Public/Private Mix: Options for Financing Targeted Universality in Long-Term Care
Bibliographic record
Abstract
The way in which we pay for long-term care (LTC) services is going to come under enormous pressure as Canada's baby boomers age. Once baby boomers start to turn 75, in 2021, the demand for LTC services will see a sharp upward trend. A number of independent projections have demonstrated how this will put pressure on the public finances in coming years. It should be concerning to Canadians that we have not publicly discussed how we will make the tough choices to cope with these pressures. Moreover, it's equally troubling that our provincial LTC systems already are unable to cope with the current level of demand for services, with less than a decade before the first wave of boomers enter age groups where demand for LTC is high, and alternate level of care patients, made up mostly of frail elderly, occupying over 15% of Canadian hospital beds on a daily basis as they await care elsewhere. Although we think it is unlikely that Canadian provinces will add LTC to the list of fully subsidized health services (hospitals and doctors), we should do a better job of targeting the existing public subsidies for LTC - and do so while putting LTC financing on a more sustainable footing.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".