RETURNED MISSING PERSONS WITH DEMENTIA: WHAT ROLE CAN FIRST RESPONDERS AND SERVICE PROVIDERS PLAY?
Bibliographic record
Abstract
Abstract The number of people living with dementia that wander and go missing is increasing. First responders and service providers play a role in the return of a missing person living with dementia. In the United Kingdom (UK), “return home interviews” are discussions between police and returned missing persons that offer support to the returned missing person to prevent repeat incidents. This study aims to explore and understand the role of first responders and service providers who follow-up with returned missing persons living with dementia. Eight service providers (e.g., social workers) and seven first responders (e.g., police officers) from Canada and the UK participated in online semi-structured interviews. Data were concurrently collected and analyzed using conventional content analysis. In the UK, police conduct “return home interviews” within 72 hours of the missing person’s return. Some charities conduct interviews with vulnerable populations to prevent repeat missing incidents by understanding the circumstances of the missing incident and connecting the person to community supports. In Canada, although follow-up with returned missing persons is not routine, some police units offer support to returned missing older adults. Government and community support organizations also offer supports to returned missing older adults such as referrals for in-home support, technologies, and vulnerable person registries. Service providers and first responders have an important role to play in the prevention of repeat missing incidents. Findings will contribute to the development of a Canadian practice guide for conducting interviews with returned missing persons living with dementia.
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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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".