Whole-system approaches to health and social care partnerships for the frail elderly: an exploration of North American models and lessons
Bibliographic record
Abstract
Irrespective of cross-national differences in long-term care, countries confront broadly similar challenges, including fragmented services, disjointed care, less-than-optimal quality, system inefficiencies and difficult-to-control costs. Integrated or whole-system strategies are becoming increasingly important to address these shortcomings through the seamless provision of health and social care. North America is an especially fertile proving ground for structurally oriented whole-system models. This article summarises the structure, features and outcomes of the Program of All-Inclusive Care for Elderly People (PACE) programme in the United States, and the Système de soins Intégrés pour Personnes Agées (SIPA) and the Programme of Research to Integrate Services for the Maintenance of Autonomy (PRISMA) in Canada. The review finds a somewhat positive pattern of results in terms of service access, utilisation, costs, care provision, quality, health status and client/carer satisfaction. It concludes with the identification of common characteristics which are thought to be associated with the successful impact of these partnership initiatives, as well as a call for further research to understand the relationships, if any, between whole-system models, services and outcomes in integrated care for elderly people.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".