National Consistency and Provincial Diversity in Delivery of Long-Term Care in Canada
Bibliographic record
Abstract
The aim of this article is to demonstrate the diversity in delivery of long-term care at the provincial level, within a national legislative framework that provides universal health insurance and public administration. Not all provinces have legislated provision of long-term care, but mandates for provincial long-term care programs typically address the needs of those with chronic health needs and maintain them in the community for as long as possible. Eligibility is based on common criteria of residency, health need, facility, assessment, and consent. The three common components of the service delivery system are institutional care, community-based services, and home-based services; the kinds of services within each component and the mix among them vary from province to province. There are also five common features in provincial service delivery systems: single point of entry, assessment, client classification, case management, and single administration. Throughout the article, examples from different provinces show the varying ways in which these aspects of service delivery have been addressed, and recent innovations have furthered this diversity. A detailed account of quality management systems also shows that while all provinces have adopted a common set of principles, they use a range of methods to pursue quality of care and to promote good practice.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".