Capturing the Role of Context in Complex System Change: An Application of the Canadian Context and Capabilities for Integrating Care (CCIC) Framework to an Integrated Care Organisation in the UK
Bibliographic record
Abstract
INTRODUCTION: If integrated care approaches are to be properly adapted to local contexts, a better understanding is required of key determinants of implementation and how these might be appropriately supported. PURPOSE: This study applied the Canadian Context and Capabilities for Integrating Care (CCIC) Framework to investigate factors influencing the implementation and outcomes of a complex integrated care change programme in Torbay and South Devon (TSD) and, more specifically, in one of five sub-localities, Coastal. METHODS: A case study method using embedded 'Researchers in Residence' to conduct action-based participatory research and deploying mixed qualitative methods. RESULTS: The relative importance of some domains differ between the English and Canadian studies. In this case study, physical features (structural and geographic) were found to be very pertinent to the relative success of the Coastal Locality, as were empowered clinical leadership, with readiness for change being expressed through processes and cultures that were risk-enabling, strengths-based, person-/outcome-focused. CONCLUSIONS: The CCIC Framework provided a useful tool capturing key elements of complex system change with key domains being transferable across settings, while also finding local variation in the UK. This would encourage its wider application so that further comparisons can be made of the ways in which different contextual and implementation properties impact upon delivery and outcomes.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".