Collection of Data on Persons Living With Dementia Who Go Missing: First Responder Perspectives
Bibliographic record
Abstract
Abstract While it is commonly cited that 60% of persons living with dementia (PLWD) wander, it is unclear whether this number reflects global contexts. Population aging has created a pressing need for the development of programs to mitigate the risks of PLWD from getting lost and going missing. Such programs would require a national strategy for the collection and integration of data on missing incidents involving this population. This study is a first step to inform such a strategy. The purposes were to: 1) identify approaches to data collection on missing persons incidents involving PLWD among Canadian police and search and rescue (SAR) organizations; 2) describe the foreseeable challenges associated with developing a national data collection strategy. We used generic qualitative description to generate data with fifteen key informants. Virtual semi-structured interviews were completed and transcribed verbatim. Content analysis and trustworthiness strategies guided analysis and rigor. Our findings indicate that police and SAR organizations collect a multitude of data pertaining to missing incidents involving PLWD. However, there is a lack of standardization in data collection, entry and analysis. Privacy legislation, limited resources, and incompatible data management systems pose challenges to data sharing and interoperability. Underreporting of missing incidents to police results in an underestimation of missing incidents. An intersectoral, uniform approach to data collection would enable the storage, analysis and comparison of national data. Accurate data on critical wandering can inform prevention, search strategies, resource allocation and effectiveness of programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".