The influence of assistive technologies on experiences of risk among older adults with age-related vision loss (ARVL)
Bibliographic record
Abstract
PURPOSE: Living with risk is a salient part of everyday living and although risk affects everyone, older adults are often regarded as a high-risk group, particularly older adults who are aging with a disability, such as vision loss. A prominent focus within low vision rehabilitation is the provision, and training, of older adults in the use of low vision assistive devices as a strategy to manage risks in both the home and community environment. This study aimed to unpack the influence of assistive technologies on experiences of risk among eleven older adults (aged 65+) with age-related vision loss. MATERIALS AND METHODS: This critical ethnographic study used home tours, the go-along method, and a semi-structured in-depth interview. RESULTS: The study identified five prevailing themes including: 1) Moving away from the individualization of risk; 2) The cost of assistive technologies as a risk contributor; 3) Practicing 'responsible living'; Technology as an adaptive strategy to risk taking; 4) Resisting the label of 'at risk'; The influence of technology on self-identity; and 5) Technology as a substitution versus supplement for social connectedness. CONCLUSIONS: The study findings highlight the importance of moving beyond a technico-scientific perspective of risk, in which risk is framed as an objective phenomenon located in older adults' bodies, and instead framing risk within a broader sociocultural perspective which moves our attention to those contextual or environmental factors that shape experiences of risk for older adults with vision loss.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".