IMPLEMENTATION MODELS FOR FOLLOWING UP AFTER A DEMENTIA-RELATED MISSING INCIDENT
Bibliographic record
Abstract
Abstract The rate of persons living with dementia who go missing is a growing concern, especially those who go missing repeatedly. Drawing upon the practices of other populations such as at-risk youth, following up with persons living with dementia and their supports after an incident could reveal contributing factors and also connect people with supports to mitigate the risk for going missing again. In Canada, models for such follow-ups are limited. The purpose of this presentation is to describe two implementation models for following up with persons living with dementia and their care partners after a missing incident has occurred. We conducted 20 individual semi-structured online interviews with police and service providers in Canada and the United Kingdom to understand the scope of and approaches to following up with this population. Generic qualitative description and conventional content analysis were used. We developed a resource guide that describes these approaches and proposes how follow-ups could be consistently employed. Two focus groups with police officers and community service providers (n= 11) were conducted to obtain feedback on the guide. We are now working with community partners in two Canadian provinces to develop implementation models unique to their existing processes, resources, and systems; these models put into practice approaches in the resource guide. Our presentation will showcase the guide and the models developed in collaboration with our partners. These models can guide police organizations, Alzheimer Societies, and communities who support persons living with dementia at risk of going missing.
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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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".