Innovative Integrated Health And Social Care Programs In Eleven High-Income Countries
Bibliographic record
Abstract
High-income countries face the challenge of providing effective and efficient care to the relatively small proportion of their populations with high health and social care needs. Recent reports suggest that integrated health and social care programs target specific high-needs population segments, coordinate health and social care services to meet their clients' needs, and engage clients and their caregivers. We identified thirty health and social care programs in eleven high-income countries that delivered care in new ways. We used a structured survey to characterize the strategies and activities used by these programs to identify and recruit clients, coordinate care, and engage clients and caregivers. We found that there were some common features in the implementation of these innovations across the eleven countries and some variation related to local context or the clients served by these programs. Researchers could use this structured approach to better characterize the core components of innovative integrated care programs. Policy makers could use this approach to provide a common language for international policy exchange, and this structured characterization of successful programs could play an important role in spreading them and scaling them up.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".