Developing practice standards for engaging people living with dementia in product design, testing, and commercialization – a case study
Bibliographic record
Abstract
To successfully create assistive technologies for persons with dementia, product developers must understand the capacity of people with dementia to use these technologies. Capacity assessment is typically done through user experience research. However, the published literature is bereft of guidelines to conduct optimal user experience research in samples of persons with dementia. We recruited persons with dementia from community-based organizations and private partners to participate in user experience research for an assistive technology platform. After a testing session, we used semi-structured interviews to ask participants about their involvement in the user experience process. We employed an inductive thematic approach to analyze the interview transcripts and draft guidelines to meaningfully engage persons with dementia in user experience research in the future. Ten participants with mild to moderate dementia (6 females, 4 males) participated in the study. Nine participants had previous experience with mobile devices. Thematic analysis yielded three overarching themes: 1) the techniques, approaches and attributes of the interviewer; 2) participants' views on being part of the user experience research process; and 3) specific items to optimize the research process. Resulting guidelines were divided into recommendations for the interviewer specifically, and for the broader research process.
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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 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".