Building Research Capacity With Older Adults: The Collective of Older Adult Researchers (COAR) Initiative
Bibliographic record
Abstract
Abstract Developing the capacity of older adults to meaningfully engage in research is an increasingly recognized priority in the field of aging. However, practical frameworks for effectively fostering research capacity through collaborative partnerships with older adult community members remain limited. This paper introduces the Collective of Older Adult Researchers (COAR) project, an innovative intergenerational initiative between 411 Seniors Centre Society and Simon Fraser University (Vancouver, Canada). Designed to advance the vision of community research hubs within third places that serve older adults, this initiative prioritizes key aging-related issues while increasing older adults’ direct involvement in research. Through a series of three interactive 2.5-hour workshops, participants engaged in a blend of research methodology presentations and hands-on activities conducted in community spaces. Older adults (n = 10) were paired with graduate students (n = 10) to co-develop and implement a practical research project. A reflexive analysis of the process, conducted in collaboration with older adult co-researchers, identified four key themes that offer insights for strengthening this model and informing broader implementation: (1) Engaging Community Partners in Workshop Design, (2) Fostering Intergenerational Collaboration, (3) Prioritizing Informal Collaborative Dialogue, and (4) Building Pathways for Sustainable Change. Findings highlight the potential of community-based research training to equip older adults with the knowledge and skills needed to become equitable research partners. However, further exploration is required to enhance, sustain, and scale these models, which will be investigated through forthcoming community-engaged research collaborations.
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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 | 0.095 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.047 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".