An evidence-informed, community-engaged approach to designing a large-scale, impact-oriented research funding initiative to foster the implementation of transformative integrated care: a multi-methods qualitative study
Bibliographic record
Abstract
BACKGROUND: Integrated care is a promising strategy to advance system transformation, care coordination, equity, and better health outcomes. Health services and policy research can drive evidence-informed health system improvements but is often underutilized. To optimize the relevance and impact of integrated care research as a transformative lever for better health and system outcomes, the Canadian Institutes of Health Research's Institute of Health Services and Policy Research (CIHR-IHSPR) designed a large-scale, evidence-informed, community-engaged research funding initiative. This paper outlines the approach and methods used by CIHR-IHSPR and describes how they informed the design and development of Transforming Health with Integrated Care (THINC), a large-scale, impact-oriented research funding initiative that promotes the adoption and proliferation of integrated care in Canada. METHODS: A multi-method qualitative, community-engaged approach was used to inform the design of a research funding strategy. Key features of the approach included multiple evidence inputs (retrospective and prospective information from primary [key informant interviews, focus groups, and a workshop] and secondary [CIHR funding data and literature review] sources), pan-Canadian reach of community engagement, involvement of diverse interest-holders, iterative data collection and analysis, and a commitment to identifying shared priorities through a community-engaged process. FINDINGS: There was consensus across the evidence inputs that implementing, adapting, and scaling evidence-informed integrated care interventions is crucial for real-world impact. Strategies found important for improved research relevance and impact include implementation science, rapid response, embedded research, and knowledge mobilization, along with key initiative design elements such as co-leadership, cross-jurisdictional and interdisciplinary teams, and a focus on the Quintuple Aim. Priority populations were also identified for maximizing the potential benefit and impact of the research. These findings informed the design of THINC, resulting in a multi-program initiative aligned to a shared goal of evidence-informed integrated care transformation. A collaborative design approach fostered shared objectives, commitment from multiple partner organizations, and resources to increase the initiative's size and scope. CONCLUSIONS: The study demonstrates the feasibility of using an evidence-informed, community-engaged approach and the influence and benefits of the approach in designing a large-scale research funding initiative that aims to be transformational and impactful.
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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 | 0.105 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".