Investigating Exit-Seeking Interventions for Residents with Alzheimer's Disease in Institutional Care
Bibliographic record
Abstract
The exit-seeking behavior of persons with Alzheimer's disease has been associated with a decline in the physical and emotional wellbeing of the residents and their caregivers in long-term care facilities.Recent studies in the field suggest that promoting safe wandering and adapting to the patients needs contribute for better exit-seeking management, which can be facilitated through the use of assistive technology.In order to investigate how the technology can be introduced to nursing homes to effectively deter this dangerous behavior, five research methods are utilized: expert, family and artist interviews, a co-design workshop and a visual survey.Noticing the attitudes of the research participants towards the use of technology and their interests in low-tech deterrents such as door camouflages, the study examines the rationale behind the effectiveness of various exit-seeking interventions in order to provide recommendations for the professionals involved in the development of such solutions.The research finds that an effective wandering management strategy requires a combination of multiple approaches that complement each other in terms of the purpose of use, and build on the residents' interests, understanding and backgrounds.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".