How do policy supports enable the implementation, scale, and sustainability of integrated care programs in England, Germany, and The Netherlands? Lessons for Canada
Bibliographic record
Abstract
• Integration is a policy priority, but initiatives vary in scale and impacts. • Voluntary partnerships between providers may promote uptake of new models of care. • Financing streams can support collaborative working and development of intersectoral knowledge. • Policy entrepreneurs play key role in promoting uptake and implementation. • Power and relative funding imbalances between health and social services impede collaboration. Integrated care aims to coordinate the care needs of a population, particularly individuals requiring complex care, across community, primary and secondary care settings. This study explores policy supports for integrated models of care in England, Germany, and the Netherlands to consider the implications for policy transfer for Canada. We reviewed academic and grey literature about integrated models of care across three comparator countries and conducted in-depth qualitative interviews with 14 expert informants in Autumn 2023. Results were mapped against a framework for analysis about policy supports and transfer. Integrated care initiatives varied in scale and scope with local population initiatives (Germany), devolved decision-making initiatives (England), or by addressing population subgroups (Netherlands). There are power and relative funding imbalances between the health and social services sectors that impede collaboration. Voluntary approaches to organisational governance reforms and partnerships with primary care providers promote uptake but policy entrepreneurs are crucial to facilitating implementation. Workforce adaptations and upskilling initiatives can enable interprofessional collaboration and intersectoral knowledge to address implementation gaps. There remain practical challenges with data infrastructure and sharing. Legislation is an important enabling factor for supporting governance. New financing streams can reward collaborative working for interdisciplinary teams. Policymakers at the macro- and meso-level must support policy from intention to implementation.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".