CASE SCENARIOS OF CANADIAN MEDIC-ALERT SUBSCRIBERS WITH DEMENTIA WHO GO MISSING
Bibliographic record
Abstract
Abstract With the increasing prevalence of dementia worldwide, it is expected that incidences of getting lost and going missing among persons living with dementia due to wayfinding challenges will also increase. Despite this, there is currently a gap in accessible education materials specific to risk factors and incidents of getting lost and going missing, aimed at first responders and family care partners. Case scenarios can effectively convey the circumstances of recurring events while also accounting for lived experiences. The purpose of this project was to develop case scenarios informed by real-life missing incidents that involve persons living with dementia in Canada. Qualitative description and conventional content analysis were used to analyze summary notes of missing incidents (n=515) obtained from Medic-Alert Foundation Canada hotline database. Summary notes contained: demographics of the missing person, their living situation and circle of support, events leading up to incident, by whom and where they were found, their health condition, and how they were re-united with their care partners. Case scenarios were developed that reflected the common (e.g., going missing while on foot, being found on the street) as well as less common (e.g., going missing while driving, being found seriously injured). Five experts (person living with dementia, care partner, health care professional, support providers) reviewed these scenarios to ensure relevance. Case scenarios can raise awareness of the circumstances encountered during missing incidents. The scenarios can be used by first responders and care partners to learn how to respond to missing incidents and receive support.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".