The benefits and barriers to technology acquisition: Understanding the decision-making processes of older adults with age-related vision loss (ARVL)
Bibliographic record
Abstract
Introduction While research has investigated the factors influencing acquisition and use of technologies/assistive devices by older adults, few studies have addressed the decision-making processes regarding technology adoption of older adults with age-related vision loss. Method This critical ethnography engaged 10 older adults with age-related vision loss in narrative interviews, participant observation sessions, and semi-structured in-depth interviews to understand their decision-making processes related to the acquisition and use of low vision assistive devices to support occupational engagement. Findings Study findings focused on the benefits and barriers to technology acquisition and use. Benefits of technology acquisition included: enhanced occupational engagement; independence; safety; insurance; and validation of the disability, while the barriers to technology acquisition included: cost; training; usability; lack of awareness of low vision rehabilitation services; fear of being taken advantage of; and desire to preserve a preferred self-image. Conclusion Considering the low uptake of vision rehabilitation services, the study findings are important to occupational therapy. A better understanding of the perceived benefits and barriers to technology adoption from the perspective of older adults will help occupational therapists maximize treatment planning designed to enhance the occupational engagement of older adults aging 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".