Employing Participatory Citizen Science Methods to Promote Age-Friendly Environments Worldwide
Bibliographic record
Abstract
The trajectory of aging is profoundly impacted by the physical and social environmental contexts in which we live. While “top–down” policy activities can have potentially wide impacts on such contexts, they often take time, resources, and political will, and therefore can be less accessible to underserved communities. This article describes a “bottom–up”, resident-engaged method to advance local environmental and policy change, called Our Voice, that can complement policy-level strategies for improving the health, function, and well-being of older adults. Using the World Health Organization’s age-friendly cities global strategy, we describe the Our Voice citizen science program of research that has specifically targeted older adults as environmental change agents to improve their own health and well-being as well as that of their communities. Results from 14 Our Voice studies that have occurred across five continents demonstrate that older adults can learn to use mobile technology to systematically capture and collectively analyze their own data. They can then successfully build consensus around high-priority issues that can be realistically changed and work effectively with local stakeholders to enact meaningful environmental and policy changes that can help to promote healthy aging. The article ends with recommended next steps for growing the resident-engaged citizen science field to advance the health and welfare of all older adults.
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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".