Comparative case studies in integrated care implementation from across the globe: a quest for action
Bibliographic record
Abstract
BACKGROUND: Integrated care is the coordination of general and behavioral health and is a highly promising and practical approach to improving healthcare delivery and patient outcomes. While there is growing interest and investment in integrated care implementation internationally, there are no formal guidelines for integrated care implementation applicable to diverse healthcare systems. Furthermore, there is a complex interplay of factors at multiple levels of influence that are necessary for successful implementation of integrated care in health systems. METHODS: Guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework (Aarons et al., 2011), a multiple case study design was used to address two research objectives: 1) To highlight current integrated care implementation efforts through seven international case studies that target a range of healthcare systems, patient populations and implementation strategies and outcomes, and 2) To synthesize the shared and unique challenges and successes across studies using the EPIS framework. RESULTS: The seven reported case studies represent integrated care implementation efforts from five countries and continents (United States, United Kingdom, Vietnam, Israel, and Nigeria), target a range of clinical populations and care settings, and span all phases of the EPIS framework. Qualitative synthesis of these case studies illuminated common outer context, inner context, bridging and innovation factors that were key drivers of implementation. CONCLUSIONS: We propose an agenda that outlines priority goals and related strategies to advance integrated care implementation research. These goals relate to: 1) the role of funding at multiple levels of implementation, 2) meaningful collaboration with stakeholders across phases of implementation and 3) clear communication to stakeholders about integrated care implementation. TRIAL REGISTRATION: Not applicable.
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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 | 0.149 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.008 | 0.020 |
| 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".