Optimizing older adult co-researchers’ involvement in PAR: proposed evaluation tool
Bibliographic record
Abstract
Purpose This paper aims to share a practical evaluation tool intended to guide and support the participation of older people in PAR projects. Participatory action research (PAR) studies with older adults have been increasing over the past ten years. Scientific evidence provides key principles for PAR projects to achieve meaningful participation by older people; however, respecting the ideals of PAR is not always straightforward. Design/methodology/approach This paper presents a case study that evaluated the involvement of nonacademic researchers in a PAR project using an evaluation tool derived from a literature review of PAR undertaken with this population (Corrado et al., 2020). The study goals were first to assess the assets and limits of the older co-researchers’ participation within the PAR project, and second to provide a revised version of the evaluation tool to support future PAR with older people. First, the authors designed an evaluation tool for nonacademic participation in PAR studies by older people that covers three main themes: older people positioned as prominent research partners; symmetrical power relations between academic and nonacademic researchers; and commitment regarding inclusiveness and long-term collaboration. Second, the authors performed an evaluation using this tool within the Active Aging with Dignity PAR Project. Findings Third, the authors used the results of this experiment to suggest improvements for an enhanced version of the evaluation tool aiming at supporting fuller involvement of older nonacademic researchers in PAR studies. Originality/value To the authors’ knowledge, this evaluative tool is a methodological innovation in gerontology.
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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 | 0.285 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".