Technology for Healthy Aging and Wellbeing: Co-producing Solutions
Bibliographic record
Abstract
Methods to facilitate co-production in mental health are important for engaging end users. As part of the Technology for Healthy Aging and Wellbeing (THAW) initiative we organized two interactive co-production workshops, to bring together older adults, health and social care professionals, non-governmental organizations, and researchers. In the first workshop, we used two activities: Technology Interaction and Scavenger Hunt, to explore the potential for different stakeholders to discuss late life mental health and existing technology. In the second workshop, we used Vignettes, Scavenger Hunt, and Invention Test to examine how older adults and other stakeholders might co-produce solutions to support mental wellbeing in later life using new and emerging technologies. In this paper, we share the interactive materials and activities and consider their value for co-production. Overall, the interactive methods were successful in engaging stakeholders with a broad range of technologies to support mental health and wellbeing and in co-producing ideas for how they could be leveraged and incorporated into older people's lives and support services. We offer this example of using interactive methods to facilitate co-production to encourage greater involvement of older adults and other under-represented groups in co-producing mental health technologies and services.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".