Creating an integrated innovation system to enable the adaptation and uptake of health-system innovations in Canada: insights from citizen panels and a national stakeholder dialogue
Bibliographic record
Abstract
BACKGROUND: Health-system leaders are increasingly faced with making decisions about whether and how to use a wide range of current and emerging health-system innovations to address complex system and policy challenges. Health-system innovations can broadly include new ways of doing things at a system level, such as new approaches to govern health systems, care delivery, funding models, health policy or better ways to integrate health and social services. However, Canada has historically struggled with the adaptation and uptake of health-system innovations. This multicomponent study aimed to explore the challenges, approaches and implementation considerations for creating an integrated innovation system that enables the adaptation and uptake of health-system innovations from the perspectives of citizens and health system leaders in Canada. METHODS: We synthesized the best-available evidence into an evidence brief and a subsequent plain-language version (a citizen brief) in consultation with a steering committee and key informants, including policymakers, leaders of systems, organizations and professional organizations, industry representatives, citizen leaders and researchers. These briefs informed deliberations in four citizen panels (n = 48 participants) and a national stakeholder dialogue with health-system leaders (n = 23 participants) to identify key challenges, approaches, implementation considerations and next steps that could be taken. RESULTS: Citizen panel participants and health-system leaders highlighted barriers such as culture and mindsets that resist health-system innovations, limited targeted funding for health-system innovations and processes that encourage sustainability, lack of mechanisms to adapt health-system innovation in local contexts and limited health human resources due to competing interests across health systems. Both groups emphasized the need for people-centred approaches to establish shared goals and vision, identify gaps and map what has worked to drive health-system innovations, set priorities and discuss how each stakeholder group can contribute to building and reviewing implementation considerations such as resources and funding related for the adaptation and uptake of health-system innovations. CONCLUSIONS: The findings provide insight for ongoing efforts to improve the development, implementation and evaluation efforts to enhance and harness health-system innovation to strengthen health systems in Canada. Collaboration from within and between governments and sectors will ultimately help to increase the value gained from health-system innovations.
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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 | 0.059 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.044 | 0.020 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".