Research Practices to Foster Accessibility for, and the Inclusivity of, Older Adults With Vision Loss: Examples From a Critical Participatory Study
Bibliographic record
Abstract
Introduction: It is important that the embodied experience of visual impairment is understood in research, and critical participatory action research (CPAR) is one such research methodology that encourages the direct participation of individuals with disabilities in the design, development, and dissemination of research. Methods: This reflexive paper unpacked how our research collective carried out accessible and inclusive CPAR with older adults with vision loss. Using meeting notes, group discussions, and reflection as the primary data collection methods, the research collective set out to answer the following question(s): (1) How has the research collective worked to enact an inclusive and accessible CPAR study that supports the full participation of older adults with vision loss? And (2) What has the research collective learned through reflecting on the process regarding challenges and strategies for working towards an inclusive and accessible CPAR? Results: Methodological decisions made by the research collective to ensure an accessible and inclusive CPAR with older adults with vision loss were broken down into four key stages in the CPAR process including: (1) study planning stage; (2) data collection stage; (3) data analysis stage; and (4) knowledge mobilization (KMb) stage. Discussion: Our discussion highlights what has been learned, with respect to building inclusive research practices. Such key learnings are centered around the idea that there is no one-size-fits-all approach to building inclusive research practices, but rather a dedication to offering choice, autonomy, and control, in combination with flexibility, are paramount to building inclusive CPAR. Lastly, this paper unpacked those long-standing traditions, such as obstructive ethics processes and funding restrictions, that need to be addressed to support inclusive CPAR moving forward.
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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 | 0.042 | 0.029 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; both teacher heads 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".