"Take a walk in my shoes": A narrative account of the experiences of community mobility among older adults living with age-related vision loss (ARVL)
Bibliographic record
Abstract
This study aimed to co-construct the accounts of older adults with age-related vision loss (ARVL) regarding their community mobility experiences. The study was based on a constructivist paradigm, and the collection and analysis of data adopted the narrative methodology. Participants included four older adults with one of the following conditions: macular degeneration, glaucoma, and/or diabetic retinopathy; all were at least 60 years old. Participants were recruited from Optometry clinics in London, Ontario, with one participant recruited using snowball sampling. The collection of data comprised three narrative interviews, all of which were audio recorded. These interviews took place over the phone as per the older adults’ request. This study conducted thematic and structural narrative analyses (Riesman, 2008) on participants' stories and identified six dominant themes, including: (1) Moving from private vehicles to public transport, (2) Elements of the physical environment act more as barriers than facilitators to community mobility, (3) The use of assistive devices and compensatory strategies to support community mobility, (4) Social networks and their influence on community mobility, (5) Ableist perceptions of older adults with ARVL & its impact on community mobility, and (6) Community mobility barriers stemming from political factors. The research findings expand our understanding of the community mobility experiences of older adults with ARVL and highlight the benefits of more inclusive age-friendly environment in facilitating their community mobility. The study's future directions and implications are also discussed.\nKeywords: Age-related vision loss, older adults, environment, community mobility
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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 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".