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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".