A Critical Ethnographic Approach for Understanding How Environmental Factors Shape Experiences of Community Mobility for Older Adults Aging With and Into Vision Loss: A Protocol Paper
Bibliographic record
Abstract
The number of older adults diagnosed with vision loss in Canada, and globally, is growing. Older adults with age-related vision loss (ARVL), including macular degeneration, glaucoma, and diabetic retinopathy, experience a reduced capacity to engage with their communities because their opportunities to access and engage with their environments are greatly reduced. This can lead to feelings of abandonment, enhanced fear, and social isolation, which can have determinantal impacts on physical, social, and psychological health and well-being. This critical ethnographic study moves beyond exclusively identifying physical barriers to community mobility, towards addressing the physical, political, social, and pragmatic barriers to community mobility that are limiting for older adults aging with, and into, vision loss. As a research collective consisting of older adults with vision loss, low vision service providers, policy makers, and Blind and sighted academics, we developed a critical ethnographic study which consists of three qualitative interviews. Our protocol outlines how data for this study were collected, including how methods were adapted to support people experiencing vision loss, analyzed, and managed to ensure best privacy practices. We also describe our approach to engaging in reflexive data analysis and interpretation, including collective data analysis sessions.
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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 | 0.051 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".