‘That's for old so and so's!’: does identity influence older adults’ technology adoption decisions?
Bibliographic record
Abstract
Abstract The role of identity in older adults’ decision-making about assistive technology adoption has been suggested but not fully explored. This scoping review was conducted to understand better how older adults’ self-image and their desire to maintain this influence their decision-making processes regarding assistive technology adoption. Using the five-stage scoping review framework by Arksey and O'Malley, a total of 416 search combinations were run across nine databases, resulting in a final yield of 49 articles. From these 49 articles, five themes emerged: (a) resisting the negative reality of an ageing and/or disabled identity; (b) independence and control are key; (c) the aesthetic dimension of usability; (d) assistive technology as a last resort; and (e) privacy versus pragmatics. The findings highlight the importance of older adults’ desire to portray an identity consistent with independence, self-reliance and competence, and how this desire directly impacts their assistive technology decision-making adoption patterns. These findings aim to support the adoption of assistive technologies by older adults to facilitate engagement in meaningful activities, enable social participation within the community, and promote health and wellbeing in later life.
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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 | 0.026 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".