The contrasting role of technology as both supportive and hindering in the everyday lives of people with mild cognitive deficits: a focus group study
Bibliographic record
Abstract
BACKGROUND: It is well known that people with mild cognitive deficits face challenges when performing complex everyday activities, and that the use of technology has become increasingly interwoven with everyday activities. However, less is known of how technology might be involved, either as a support or hindrance, in different areas of everyday life and of the environments where challenges appear. The aim of this study was to investigate the areas of concern where persons with cognitive deficits meet challenges in everyday life, in what environments these challenges appear and how technology might be involved as part of the challenge and/or the solution to the challenge. METHODS: Data were gathered through four focus group interviews with participants that live with cognitive deficits or cohabit with a person with cognitive deficits, plus health professionals and researchers in the field. Data were transcribed, coded and categorized, and finally synthesized to trace out the involvement of technology. RESULTS: Five areas of concern in everyday life were identified as offering challenges to persons with cognitive deficits: A) Managing personal finances, B) Getting around, C) Meeting family and friends, D) Engaging with culture and media and, E) Doing everyday chores. Findings showed that the involvement of technology in everyday activities was often contrastive. It could be hindering and evoke stress, or it could bring about feelings of control; that is, being a part of the solution. The involvement of technology was especially obvious in challenges linked to Managing personal finances, which is a crucial necessity in many everyday activities. In contrast, technology was least obviously involved in the area Socializing with family and friends. CONCLUSIONS: The findings imply that technology used for orientation and managing finances, often used outside home, would benefit from being further developed in order to be more supportive; i.e. accessible and usable. To make a positive change for many people, the ideas of inclusive design fit well for this purpose and would contribute to an age-friendly society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".