Development of a locator device usability scale for persons with dementia at risk of getting lost
Bibliographic record
Abstract
Abstract There is an increasing number of persons living with dementia who live alone. Recent COVID-19 pandemic restrictions have resulted in more persons receiving care remote through information and communication technologies. Locating technologies can be a tool to help care partners monitor their loved ones living with dementia. These devices can also mitigate risks associated with going missing, by reducing time for search and returning the lost person home safely. However, there is no clear, standardized approach to assess the usability of these devices. The purpose of this study was to develop a locator device usability scale for persons living with dementia at risk of getting lost. A two-phase study that utilized a multi-method design and included participatory and iterative strategies was conducted. In the first phase, an item pool was generated through online focus groups with service providers, technology developers, care partners and persons living with dementia. The second phase refined the item pool using an online survey and online focus groups with the same stakeholder groups. Five overarching categories were identified as important for the usability of locating device: features, inclusivity, simplicity, aesthetic appeal, and ethics. Participants identified the need for multiple versions of the usability scale including one specifically for persons living with dementia. The newly developed locator device usability scale can enhance the acceptance of these devices, thereby supporting remote caregiving and promote the safety and autonomy of persons living with dementia.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".