Return Home Interviews for Missing Older Adults With Dementia: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction: Persons living with dementia are at risk of becoming lost. When a person is returned home safely after a missing incident, an interview with the person or care partner may identify ways to prevent repeat incidents. It is not known if these interviews are being conducted for this population. Objectives: The purpose of this review was to understand return home interviews and whether they are being used with persons who have dementia. Methods: Scholarly and grey literature were searched in 20 databases. Articles were included from any language, year, study design if they included terms resembling “return home interview”, “missing,” “lost,” or “runaway”. Results: Eleven articles in scholarly, and 94 in grey literature sources were included, most from the United Kingdom. The majority of academic (55%) and grey (61%) articles were related to missing children, and none were specifically about persons living with dementia. Interviews were typically conducted within 72 hours after a missing person was returned, and by police or charitable organizations. The main reasons were to understand the causes of the incident and confirm the missing person’s safety, identify support needs, and to provide support to reduce repeat missing incidents. Conclusion: Existing reasons for interviews can also apply to persons with dementia. This review informs future research on return home interviews. It also informs community organizations, and police services interested in adopting this practice with persons living with dementia. Evaluations would confirm if these interviews can reduce repeat incidents and help keep people with dementia safe.
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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 | 0.024 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".