The Coping Strategies of Older Adults with Age Related Vision Loss (ARVL) – A Narrative Account
Bibliographic record
Abstract
The goal of this study was to share the stories of older adults with age-related vision loss (ARVL) and how they have coped to maintain meaningful occupational engagement. Grounded in a constructivist paradigm, data collection and analysis were guided by the narrative inquiry methodology. The participants consisted of six older adults aged 60 or older, diagnosed with one of the following ARVL conditions: macular degeneration, diabetic retinopathy, and/or glaucoma. Participants were recruited from vision loss non-profit organizations such as the Canadian National Institute for the Blind (CNIB) and the Alliance for Equity of Blind Canadians (AEBC). One older adult was recruited through snowball sampling, and two were participants in previous research conducted in the Vision Loss in Later Life Research Lab (VITAL). Data collection occurred across three narrative interviews. Each of these interviews were audio recorded, and semi-structured. These interviews took place both over the phone or in person, as per the older adult’s request. Fraser’s (2004) line-by-line method was employed to produce a thorough thematic analysis based on the stories shared by each of the older adults. Three main themes were identified, and coping mechanisms were grouped by family including: (1) Psychological coping mechanisms, (2) Social coping mechanisms and, (3) Behavioural coping mechanisms. This research expands knowledge on how older adults cope with ARVL and the importance of maintaining meaningful occupation for older adults with vision loss. The future directions and implications of the research are discussed and unpacked as well.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".