The environmental factors that influence technology adoption for older adults with age-related vision loss
Bibliographic record
Abstract
Introduction With the increasing proportion of older adults aging with vision loss, low-vision assistive devices can help to support occupational engagement; however, such devices are grossly underused among this population. The overarching purpose of this project was to examine the environmental factors that influence technology adoption for older adults with age-related vision loss. Methods A one-day workshop, which utilized a variety of hands-on methods including Show & Tell, Technology Interaction, and an “App” Assessment activity, was conducted. A total of 19 participants attended the workshop, including 10 older adults with age-related vision loss, six caregivers, one healthcare provider, and two technology industry professionals. Results A total of four themes emerged, including: (1) making life harder; (2) relying on support networks; (3) factoring in the pragmatics; and (4) not me, not yet. These themes illustrate the various ways that environmental factors, including physical, social, cultural, and institutional/political factors, influence decision-making regarding technology adoption by older adults with age-related vision loss. Conclusion This paper demonstrates several environmental factors that influence low-vision assistive device adoption among older adults with age-related vision loss. With their holistic view of clients, including an appreciation for environmental influences, occupational therapists are well positioned to help identify those environmental barriers limiting low-vision assistive device adoption and use.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".