ENGAGING OLDER ADULTS AND THEIR CAREGIVERS IN INNOVATION ECOSYSTEMS FOR HEALTH AND AGING
Bibliographic record
Abstract
Innovation for health and aging offers potential benefits for the well-being of older adults and their caregivers. Regional Innovation Ecosystems (RIEs), involving a “triple helix” of industry, government and academic stakeholders, have been proposed to support development and commercialization of innovations. We sought to understand how older adults and their caregivers contribute their perspectives to RIEs for health and aging, and whether their role could be enhanced through an evolution of the triple helix partnership. A three-phase integrated mixed-methods study, emphasizing stakeholder engagement was conducted. Phase one involved a scoping review on user engagement in RIEs. Building on this, phase two engaged older adults and their caregivers (n=15), and representatives from the triple helix (n=21) in individual and group interviews. Following Kane and Trochim’s (2007) Concept Mapping methodology, phase three integrated themes into a framework of priorities. We found that there is currently little meaningful involvement of older adults and their caregivers in RIEs. Evolving the triple helix theoretical framework to accommodate the growing importance of meaningful engagement of older adults and their caregivers will require a recognition of the need for diversity of representation, consideration of barriers such as system constraints and traditional partnerships, and appreciation of multiple roles that older adults could play in health and aging innovation. This study identified directions and strategies for enhanced engagement in RIEs for health and aging. We are continuing to collaborate with project stakeholders to develop RIEs that can support the health and well-being of older adults and their caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".