Engagement of older adults in regional health innovation: The ECOTECH concept mapping project
Bibliographic record
Abstract
Objectives: Regional health innovation ecosystems can activate collaboration and support planning, self-management and development and commercialization of innovations. We sought to understand how older adults and their caregivers can be meaningfully engaged in regional health innovation ecosystems focused on health and aging–related technology innovation. Methods: A six-phase concept mapping technique gathered data over six time points across Canada. Brainstorming conducted online and in person identified engagement ideas. Statements were sorted by similarity and rated by participants on importance and feasibility. Qualitative approaches and multidimensional scaling, hierarchical cluster analysis, descriptive statistics and t tests were used for analysis. Results: Sixty-two unique ideas were assembled into a seven-cluster framework of priorities for engagement in regional health innovation ecosystems including public forums, co-production and partnerships, engagement, linkage and exchange, developing cultural capacity, advocacy and investment in the ecosystem. Conclusions: This study identified a framework of priorities for directions and strategies for older adult and caregiver engagement in regional health innovation ecosystems. Next steps include collaborations to develop regional health innovation ecosystems that actively engage older adults and their caregivers in health and aging–related technology innovation.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".