P3‐529: BEST PRACTICES USED BY POLICE SERVICES IN THE SEARCH FOR MISSING OLDER ADULTS WITH DEMENTIA
Bibliographic record
Abstract
Strategies, such as locator devices using global positioning systems, offer options for police and family to find missing persons with dementia. If devices are not worn by missing persons, alternative interventions may help in the rapid response to locate them. To date, no literature review has exhaustively searched what types of police practices are used to locate vulnerable older adults with cognitive impairment. The purpose of this scoping literature review was to examine the range and extent of strategies used by police for locating missing persons with dementia. Using Arksey and O'Malley's (2005) protocol, articles were identified through searches of 7 scholarly and 5 grey literature databases and were included if they addressed policing issues and strategies related to finding missing persons living with dementia. Strategies could be lower or higher complexity, and supported police public education or search and rescue practices. Data from the studies were analyzed descriptively. The literature search identified 16 studies and 18 websites. Strategies originated from 6 countries, with the majority being from the United States (45%) and Canada (33%). Out of the included strategies, 10 subcategories were evident; the most common were: education, search strategies/procedures, identification, community engagement and locating technologies. Of the 16 included studies, five (31.3%) evaluated the usability or effectiveness of the strategies, three of which were conducted in real world settings involving persons living with dementia. As only 3 (18.75%) of the research studies were conducted in real world settings with actual users with dementia, the validity of these strategies as actual “best practices” remains to be verified. Also, it is not possible to state with certainty which practices are acceptable and consistently adopted by police services across specific jurisdictions. Research that demonstrates the usability and effectiveness of the 10 types of strategies would inform police services on implementation of best practices. Ideally, these best practices would be applied consistently on national and international levels. Results from this literature review will be included in the development of a guideline of best practices and resources to be disseminated by the Alzheimer Society of Ontario in March 2019.
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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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".