A Protocol Paper on the Preservation of Identity: Understanding the Technology Adoption Patterns of Older Adults With Age-Related Vision Loss (ARVL)
Bibliographic record
Abstract
There are a growing number of older adults with age-related vision loss (ARVL) for whom technology holds promise in supporting their engagement in daily activities. Despite the growing presence of technologies intended to support older adults with ARVL, there remains high rates of abandonment. This phenomenon of technology abandonment may be partly explained by the concept of self-image, meaning that older adults with ARVL avoid the use of particular technologies due to an underlying fear that use of such technologies may mark them as objects of pity, ridicule, and/or stigmatization. In response to this, the proposed study aims to understand how the decision-making processes of older adults with ARVL, as it relates to technology adoption, are influenced by the negotiation of identity. The study protocol will justify the need for this critical ethnographic study; unpack the theoretical underpinnings of this work; detail the sampling/recruitment strategy; and describe the methods which included a home tour, go-along, and semistructured in-depth interview, as well as the collective approach taken to analyze the data. The protocol concludes by examining the ethical tensions associated with this study, including a focus on the methods adopted as well as the ethical challenges inherent when working with an older adult population experiencing 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 | 0.091 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".