A balancing act: exploring ethical and legal concerns associated with release of personal information in alert systems for missing persons with dementia
Bibliographic record
Abstract
BACKGROUND: Technology, such as alert systems, can foster community engagement in locating missing persons with dementia and minimize potential harm. However, concerns arise about implications of public disclosure of missing individual's personal information (such as age, photographs, physical descriptions, and medical conditions) within alert systems. Until now, there has been no review of these concerns, particularly in the Canadian context. Our study aimed to explore community members' perspectives on the ethical and legal concerns associated with the release of personal information in alert systems for missing persons with dementia. METHODS: Using a qualitative descriptive approach, we conducted semi-structured interviews with 18 participants: people living with dementia, care partners, service providers, first responders, and experts in ethics, policy, and the law from Canada and the United Kingdom. We conducted a thematic analysis of the interview data to inductively explore ethical and legal concerns. RESULTS: Our findings identified the following concerns: Balancing safety and privacy, stigmatization, risk of victimization and abuse, and informed consent. There is a challenge of balancing safety with privacy due to the urgency of locating missing persons when sharing personal information publicly. Disclosure of personal information, such as cognitive impairment, can increase the risk of stigmatization, victimization, and abuse for both the missing individuals and their care partners. Unfortunately, conversations about alert systems and consent do not typically occur before someone goes missing, even though people living with dementia have the right to participate in these conversations. CONCLUSIONS: Alert systems can promote community involvement in locating missing persons with dementia but must balance safety and privacy concerns. Implementation of education and policies would mitigate stigmatization, victimization, and abuse. Early conversations with people living with dementia and their care partners to understand their preferences, along with an advance consent process, can help address consent concerns. Our framework, which emphasizes ethical and legal considerations, can guide policy, practice, and decision-making to support the autonomy of people living with dementia. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".