Experiential Value of Technologies: A Qualitative Study with Older Adults
Bibliographic record
Abstract
This study investigated the experiences of older adults with technologies they own and determined how they value them. Thirty-seven older adults participated in a Show and Tell co-creation session at a one-day workshop. Participants described why they loved or abandoned technologies they own. Their responses were recorded and analysed using Atlas.ti 22.0.0. Seven main themes representing experiential value in older adults emerged from the analysis: Convenience, Economy, Learning and Support, Currency of Technology, Privacy and Security, Emotions and Identity aspects of their experiences. This qualitative study has resulted in implications to design that recommends (a) Design for product ecosystems with technologies and services well-coordinated and synchronized to facilitate use of the technology (b) Create awareness and information on privacy and security issues and technical language associated with it (c) Make anti-virus and anti-phishing software accessible to older population (d) Design technologies as tools that allow older adults to identify themselves in the community and family (e) Create services that make technologies and services in the ecosystem affordable for the older adults. The outcomes of this study are significant as they provide recommendations that target systemic issues which present barriers in the use of technology.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".