Comparing International Models of Integrated Care: How Can We Learn Across Borders?
Bibliographic record
Abstract
INTRODUCTION: Providers, managers, health system leaders, and researchers could learn across countries implementing system-wide models of integrated care, but require accessible methods to do so. This study assesses if a common framework could describe and compare key components of international models of integrated care. THEORY AND METHODS: A framework developed for an international study of programs that address high needs high cost patients was used to describe and compare 11 case studies analyzed in two international research projects; the Implementing Integrated Care for Older Adults with Complex Health Needs (iCOACH) study in Canada and New Zealand, and the Vilans research group exploring models in the Netherlands. Comparative summaries were generated, with findings discussed at a 2019 International Conference on Integrated Care workshop. RESULTS: The template was found to be useful to compare integrated case analyses in different contexts, and stands apart from other case comparison approaches as it is easily applied and can provide practical guidance for frontline staff and managers. Areas of improvement for the template are identified and two updated versions are presented. CONCLUSIONS AND DISCUSSION: There is value to using a common template to provide guidance in international comparison of models of integrated care. We discuss the applicability of the approach to support scale and spread of integrated care internationally.
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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 | 0.183 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.025 | 0.053 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".