Integrating care for older people with complex needs: key insights and lessons from a seven-country cross-case analysis
Bibliographic record
Abstract
BACKGROUND: To address the challenges of caring for a growing number of older people with a mix of both health problems and functional impairment, programmes in different countries have different approaches to integrating health and social service supports. OBJECTIVE: The goal of this analysis is to identify important lessons for policy makers and service providers to enable better design, implementation and spread of successful integrated care models. METHODS: This paper provides a structured cross-case synthesis of seven integrated care programmes in Australia, Canada, the Netherlands, New Zealand, Sweden, the UK and the USA. KEY FINDINGS: All seven programmes involved bottom-up innovation driven by local needs and included: (1) a single point of entry, (2) holistic care assessments, (3) comprehensive care planning, (4) care co-ordination and (5) a well-connected provider network. The process of achieving successful integration involves collaboration and, although the specific types of collaboration varied considerably across the seven case studies, all involved a care coordinator or case manager. Most programmes were not systematically evaluated but the two with formal external evaluations showed benefit and have been expanded. CONCLUSIONS: Case managers or care coordinators who support patient-centred collaborative care are key to successful integration in all our cases as are policies that provide funds and support for local initiatives that allow for bottom-up innovation. However, more robust and systematic evaluation of these initiatives is needed to clarify the 'business case' for integrated health and social care and to ensure successful generalization of local successes.
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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 | 0.045 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| 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".