CO-CREATING RESOURCES TO RAISE AWARENESS OF RISKS OF GOING MISSING AMONG PERSONS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Persons living with dementia are at risk of getting lost and going missing due to wayfinding challenges. Yet, there are few accessible resources available that raise awareness of the risks of going missing and strategies to manage these risks. The purpose of this project was to develop engaging and informative learning resources for first responders, search and rescue personnel, persons living with dementia, care partners, and health and social care providers. Co-design and co-production approaches guided the development of these resources and were informed by reviews of the academic and grey literature, dialogue with persons with lived experiences, and content analysis of missing person cases. We produced three sets of resources: 1) nine dementia-friendly first responder videos for police and search and rescue personnel to enhance their awareness of dementia and risks of going missing; 2) a toolkit of strategies and resources for persons living with dementia and care partners to reduce the risks associated with getting lost and going missing; 3) eight personas and scenarios that depict contextual factors within missing events that can be utilized by service providers and the public in their interactions with persons living with dementia and care partners. All resources were reviewed by participants through interviews, focus groups, and written feedback to identify essential content and ensure accuracy, relevance, and comprehensibility. These sets of learning resources can elevate awareness and offer practical strategies to support persons living with dementia, care partners, first responders, and service providers.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".