WHAT DOES THE LITERATURE SAY ABOUT USES OF STRATEGIES TO MANAGE WANDERING IN PERSONS WITH DEMENTIA? A SCOPING REVIEW
Bibliographic record
Abstract
Three out of five persons with dementia will wander and become lost, raising concern as to how it can be managed effectively. While interventions may help in the management of wandering, no review has included an exhaustive search on types of high and low technological solutions used to address the risks of wandering. Therefore, the purpose of this review was to examine the range and extent of high and low technological (tech) strategies used to manage wandering behaviour in persons with dementia. Articles were identified through searches of six databases, and were included if they addressed wandering in older adults. Strategies could range from low to high complexity, support independence, and consider adverse outcomes associated with wandering. The literature describes 118 articles and 129 commercial products. High tech interventions are more common in the literature (57%) than low tech solutions (43%) suggesting the growing popularity of high tech solutions to manage wandering. High tech strategies range from commercial home alarm and monitoring products to locator devices, whereas low tech strategies range from signage, traditional locks and camouflaged exit doors, to exercise, music and distraction therapies. While effectiveness of 49 interventions and usability of 13 interventions were clinically tested, most were only evaluated in institutional or laboratory settings, few addressed ethical issues, and the overall level of scientific evidence from these outcomes is low. Therefore, more rigorous research is recommended to demonstrate the efficacy of these wander-management strategies and their feasibility in community or home environments.
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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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| 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".