ACCURACY AND USABILITY OF A MOBILE ALERT SYSTEM FOR COMMUNITY CITIZENS TO LOCATE PERSONS WITH DEMENTIA WHO GET LOST
Bibliographic record
Abstract
Abstract Three out of five persons with dementia wander. While the literature supports community engagement in the location of lost older adults, publically funded Silver Alert programs are associated with jurisdictional issues and alert fatigue. In collaboration and consultation with key stakeholders (older adults living with dementia, care partners, service providers, advocates, police organizations), we developed Community ASAP, an alert system (mobile website and app) that mobilizes community citizens who volunteer to keep watch for persons with dementia reported to be lost. The purpose of this study was to evaluate usability and functionality of the Community ASAP app. Thirteen participants from six regions in Canada received a total of 130 missing person notifications. They recorded the time and content of these notifications, completed the Website Usability Questionnaire, and provided written feedback or participated in a group interview about their experiences using the app and suggestions for improvements. The Community ASAP app delivered notifications with 100% accuracy and received messages from participants with 98% accuracy. Overall, participants thought the interface was easy to navigate, graphics were pleasing, easy to use, and was clearly organized. Suggestions for improvements to increase usability included: 1) Multi-sensory alerts to make them more noticeable and increase readability; 2) Clearer navigation within the home screen and preferences; 3) User support (instructions, demo video, technical support). Evaluation results for this innovative app were favourable; suggestions will be used to further improve usability, particularly for end users who are novices at using mobile applications.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".