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 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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".