Doing Aging Research Together: Innovative Perspectives on Participatory Approaches
Bibliographic record
Abstract
Abstract The symposium aims to take a closer look at what it means to involve older participants in ageing research - beyond the role of research subjects. By discussing projects that deploy different participatory approaches we investigate the manifold ways in which older adults can become co-creators of the research process. We do so comparing such approaches in different domains, with different outcomes and in different stages of the research process. Consequently, this symposium (1) looks at the research process through the lens of benefits and challenges resulting from involving older adults as co-creators; (2) showcases projects across different domains and different jurisdictions that applied participatory approach in ageing research to discuss benefits and challenges, and (3) advances scientific insights into participatory approaches involving older adults. After an introductory contribution outlining theories, concepts and developments of participatory approaches in ageing research, we present insights from three empirical studies in different cultural and thematic settings. In our first presentation, Anna Wanka and Anna Urbaniak open the symposium by presenting an overview of participatory approaches that involve older adults. In the first empirical presentation, Julia Nolte and Hamid Turker discuss the process of involving older adults in data analysis and therefor present data from the US. In the third presentation, Lillian Hunn highlights how the recent COVID-19 pandemic impacted patient involvement in research in Canada. Finally, Anna Urbaniak discusses the process of planning participatory research with hard to reach population among older adults in Austria, namely those who are socially excluded.
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 | 0.138 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.025 | 0.139 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".