A Fifth Option for Funding Long-Term Care in Canada – Shift the Resources from Medical Treatment and Universal Pension Entitlements
Bibliographic record
Abstract
Needs for non-medical residential care services, long-term care (LTC), will increase over the next 30 years as Canada's population ages. Adams and Vanin (2016) explore four options for raising the public and private monies required to meet LTC needs. In this commentary, I raise a fifth option for finding the resources to meet emerging LTC needs. An alternative approach is to divert resources from Canada's well-resourced, but inefficient, medical treatment system. The dividend of provinces pursuing long overdue reforms to medicare is the liberation of public funds to finance emerging priorities for Canadians like LTC.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.088 | 0.063 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".