ETHICAL AND LEGAL CONSIDERATIONS WITH THE RELEASE OF PERSONAL INFORMATION USING A COMMUNITY AREA SILVER ALERT SYSTEM
Bibliographic record
Abstract
With increasing prevalence of dementia, is growing demand for strategies to help find older adults who get lost due to memory impairment. The Community Area Silver Alert Program (C-ASAP) is an alert system that invites community members to search for missing seniors. The impact of the release of private information for the purpose of finding a missing vulnerable older person remains unexplored. The objective was to identify and describe the ethical and legal issues associated with release of personal information in the C-ASAP system. Four focus groups were convened across three Canadian provinces with stakeholders including persons with mild dementia, care partners, service providers, first responders and industry representatives. Focus groups revealed concerns about social (e.g., stigma) and physical (e.g., bodily harm) with the release of the name and photograph of the missing person, particularly given their vulnerability and the potential for the information to be used in criminal activity. However, the personal information to be released by C-ASAP is comparable with other registries. In high risk situations in which a person’s life is at danger, privacy may be of less concern than in low risk situations. Consultations with relevant stakeholders are therefore necessary to ensure all privacy concerns are met. Concerns uncovered in focus groups will be used to make improvements to C-ASAP prior to its deployment in three test cities. Study findings can also be used to inform policy, and guide other alert systems and programs intended to locate missing persons 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 | 0.108 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".