Bibliographic record
Abstract
Long-term care (LTC) is but one piece of a continuum of care that stretches from living at home completely independently to the 24-hour supervision and care provided in LTC facilities. People want to stay at home as long as possible and to retain as much independence as they can, and they are often aided in doing so with informal care provided by family and friends. These realities should inform the debates around the construction of the continuum of care. Canada's spending on facility-based long-term care puts us in the middle of the pack among OECD countries; if we consider the whole of continuing care, combining LTC, supportive housing/assisted living and home-based healthcare, Canada stands out as spending the highest proportion (among the 15 countries compared) on facility-based care. Predicting the number of new long-term care beds needed in the future is difficult because of shifting needs and changes in service delivery - new stops on the continuum of care. It is quite possible to organize a system of care that includes a continuum from home-based care to supportive housing and assisted living to LTC that de-emphasizes this last step. The increasing elderly population in Canada will not bring an apocalypse to the healthcare system, but caring for this population will require a redistribution of resources to increase the emphasis on home and community-based services. Our planning for just how many (if any) new long-term care beds are needed and our assessment of reasonable access to those beds need to rest on a vision for the full spectrum of care and attention to the desires of the population we are trying to serve.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.009 |
| 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".