STRONGER TOGETHER: GLOBAL COMPARISONS OF THE USE OF CITIZEN SCIENCE TO CREATE AGE- AND DEMENTIA-FRIENDLY COMMUNITIES
Bibliographic record
Abstract
Abstract The active involvement of older people in the development of age-friendly environments has been strongly recommended. However, participatory methods with older adults remain underutilised even though older people may be more qualified to recommend what makes an age-friendly space. Using a citizen science approach in research involving older adults presents a key opportunity to have them be active participants and for researchers to obtain meaningful insight. Using this approach, older adults are viewed as co-designers. They are involved in the research process from beginning to end - research design, development of tools, data collection, analysis and knowledge dissemination. This symposium will highlight some of the work done in Canada, Australia and the United Kingdom to make communities more age-friendly using a citizen science approach. Special attention will be given to how the approach might be adapted to citizen scientists who live with dementia. After an introductory overview of the citizen science approach, the use of this method in studies including persons living with dementia and their care partners will be discussed, both in general terms and as an application to air travel. One of the presentations will also address the use of citizen science in examining population ageing and urbanization. Our symposium will conclude with a discussion of future policy, practice, and research direction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".