A Research Program on Implementing Integrated Care for Older Adults with Complex Health Needs (iCOACH): An International Collaboration
Bibliographic record
Abstract
Health and social care systems across western developed nations are being challenged to meet the needs of an increasing number of people aging with multiple complex health and social needs. Community based primary health care (CBPHC) has been associated with more equitable access to services, better population level outcomes and lower system level costs. Itmay be well suited to the increasingly complex needs of populations; however the implementation of CBPHC models of care faces many challenges. This paper describes a program of research by an international, multi-university, multidisciplinary research team who are seeking to understand how to scale up and spread models of Integrated CBPHC (ICBPHC). The key question being addressed is "What are the steps to implementing innovative integrated community-based primary health care models that address the health and social needs of older adults with complex care needs?" and will be answered in three phases. In the first phase we identify and describe exemplar models of ICBPHC and their context in relation to relevant policies and performance across the three jurisdictions (New Zealand, Ontario and Québec, Canada). The second phase involves a series of theory-informed, mixed methods case studies from which we shall develop a conceptual framework that captures not only the attributes of successful innovative ICBPHC models, but also how these models are being implemented. In the third phase, we aim to translate our research into practice by identifying emerging models of ICBPHC in advance, and working alongside policymakers to inform the development and implementation of these models in each jurisdiction. The final output of the program will be a comprehensive guide to the design, implementation and scaling-up of innovative models of ICBPHC.
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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 | 0.091 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".