Locating Missing Persons with Dementia: Using Knowledge-to-Action Framework for Implementation of Alert Systems
Bibliographic record
Abstract
Alert systems can engage the community to help locate missing persons with dementia. Evidence on the impact of implemented alert systems is minimal. Guided by three adapted Knowledge-to-Action Framework phases: identifying the problem, assessing barriers, and evaluating outcomes, this study aimed to examine understandings about alert systems and their implementation in Canada, Scotland, and the United States. A document review and interviews conducted with 40 interest holders (those with lived experience, first responders, service providers, and policymakers) underwent thematic analysis. Findings revealed variability in alert systems implementation and barriers at individual (limited understanding of alert systems, privacy concerns, alert fatigue) and organizational levels (sustainability, accessibility, privacy legislation). Participants recommended the following for successful implementation of alert systems: clear policy, collaboration, ongoing assessment, and a localized, opt-in system with accessibility, public education, and sustainable funding. This information indicates under what conditions alert systems for missing persons with dementia could be implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.078 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".