Examining the impact of familiarity on faucet usability for older adults with dementia
Bibliographic record
Abstract
BACKGROUND: Changes in cognition caused by dementia can significantly alter how a person perceives familiarity, impacting the recognition and usability of everyday products. A person who is unable to use products cannot autonomously complete associated activities, resulting in increased dependence on a caregiver and potential move to assisted living facilities. The research presented in this paper hypothesised that products that are more familiar will result in better usability for older adults with dementia. Better product usability could, in turn, potentially support independence and autonomy. METHODS: This research investigated the impact of familiarity on the use of five faucet designs during 1309 handwashing trials by 27 older adults, who ranged from cognitively intact to the advanced (severe) stages of dementia. Human factors methods were used to collect empirical and self-reported data to gauge faucets' usability. Participants' data were grouped according to cognition (i.e., no/mild, moderate, or severe dementia). Logistic regression, ranking by odds, and Wald tests of odds ratios were used to compare performance of the three groups on the different faucets. Qualitative data were used in the interpretation of quantitative results. RESULTS: Results indicated that more familiar faucets correlated with lower levels of assistance from a caregiver, fewer operational errors, and greater levels of operator satisfaction. Aspects such as the ability to control water temperature and flow as well as pleasing aesthetics appeared to positively impact participants' acceptance of a faucet. The dual lever design achieved the best overall usability. CONCLUSIONS: While work must be done to expand these findings to other products and tasks, this research provides evidence that familiarity plays a substantial role in product usability for older adults that appears to become more influential as dementia progresses. The methods used in this research could be adapted to analyse usability for other products by older adults with dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".