Bibliographic record
Abstract
Developing and implementing a national approach to home care has been an ongoing and sometimes controversial point of debate between the provinces and the federal government for a few years. We agree that Canada is ready for such a model, but the policy instruments imbedded in the model must be carefully considered in our commentary we expand on several points made by the lead paper, but focus specifically on the proposed model with respect to the suggested policy instrument recommended for financing the model and some of the key issues surrounding the delivery of home care. It is imperative from our perspective that the current political environment be fully understood to appreciate the context in which the model would be introduced. We argue t ha t understanding the complexities of home care is one thing, resolution of the issues in the model proposed is another. Although the federal government has agreed that there will be more dollars infused through the Canada Health and Social Transfer, the suggested policy instrument recommended for financing the model does not assure that home care programs will receive the funding that is required when other important and competing interests are considered (e.g., cancer treatment and waiting lists). We are at a point where there is incredible potential to reconfigure the health system such that home and community-based care can play a much greater role, but how do we fully realize that potential? The first step is to take the opportunity afforded by the huge surplus of the federal government and to invest heavily in developing home care -to create a system by design. That means thinking about funding directed to home care as an investment, and not as a cost. More funding, however, has limited utility without a sense of where the funding should be targeted, and for what purpose. We agree in principle that a national approach is required to advance and support home care to meet the needs of the future with the correct policy instruments. What happens in the next couple of years will shape that long-term model of home care. Whether that be by default or design is contingent on the willingness and commitment of the politicians and policymakers, and the degree to which the Canadian public have a voice that will be heard.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".