Identifying adoption and usability factors of locator devices for persons living with dementia
Bibliographic record
Abstract
A growing number of Canadians live with dementia. Strategies to reduce the risks of getting lost include physical barriers, restraints and medications. However, these strategies can restrict one's participation in meaningful activities and reduce quality of life. Locator devices can be used to manage safety risks while also supporting engagement and independence among persons living with dementia. As more locator devices become available on the market, adoption rates would be affected by certain factors. There is no clear, standardized approach to identify the factors that have an influence on the acceptance and usability of locator devices for persons with dementia and their care partners. This project aimed to identify factors related to acceptance and usability of locator devices that are important to individuals with dementia, their care partners, service providers and technology developers. Qualitative description and conventional content analysis guided our approach. We conducted 5 focus groups with 21 participants. Trustworthiness strategies included multiple data sources, data verification for accuracy and peer debrief. Five overarching factors emerged as critical aspects in the acceptance and usability of locator devices. These factors were inclusivity, simplicity, features, physical properties and ethics. Participants thought that locator devices do not adequately consider privacy and stigma. Therefore, the acceptance and usability of locator devices could be enhanced if privacy and stigma are addressed. The factors identified will inform the creation of an acceptance and usability scale for locator devices used by persons living with dementia, their care partners and service providers.
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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".