FACTORS ASSOCIATED WITH WANDERING AMONG PERSONS WITH DEMENTIA: A RETROSPECTIVE STUDY
Bibliographic record
Abstract
Abstract Persons living with dementia are at increased risk of getting lost and going missing due to critical wandering. Risk factors associated with critical wandering within this population are underexplored, thus prompting this study. In this retrospective observational study, we examined anonymized data from 25,785 MedicAlert® subscribers. We used a logistic regression model with a self-reported critical wandering incident as the outcome variable (p< 0.05). The average age of our sample was 75.42 (SD 14.34). Fifty-one percent (13,064/25,785) of cases had dementia and almost 22% (5,561/25,785) were involved in a critical wandering incident at least once. People living with dementia were two and a half times more likely to be involved in a missing incident compared to people without dementia (OR=2.56, 95% CI [2.39, 2.73], p< 0.001). The likelihood of being involved in a lost incident increased with advancing age; people 95-104 years old were seven times more likely to wander than those under age 65 (OR=7.11, 95% CI [5.96, 8.47], p< 0.001). Sex at birth, official Canadian languages spoken, ethnic background, population density, living arrangement, and medication were associated with increased dementia-related missing incidents. Numerous risk factors for missing incidents were identified. Our study paves the way for implementing preventative strategies to ultimately decrease the risk of going missing for person 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".