Residential Long-Term Care: Public Solutions to Access and Quality Problems
Bibliographic record
Abstract
Residential long-term care in Canada is characterized by unequal access and quality problems largely due to inadequate public funding and regulation, commercial involvement and its exclusion from medicare. Programs are patchwork, with variations across provinces in the availability of services, level of public funding, eligibility criteria and out-of-pocket costs borne by residents. Most provinces have cut long-term care bed capacity relative to the senior population in the past decade, without sufficiently expanding home and community care or adequately increasing staffing to reflect the higher acuity of the remaining residents. As a result, care is often rushed and underfunded, with poor working conditions leading to poor quality of care and quality of life for residents. This relationship between workers' and residents' well-being is well documented but poorly addressed. Also well researched but rarely reported are the negative impacts of privatization, at all levels: financing, ownership, management and delivery. This article describes the state of residential long-term care in Canada and proposes three policy directions: creating a pan-Canadian long-term care program, improving quality and reversing privatization.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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".