Guardianship and self-sovereign identity: implications for persons living with dessmentia
Bibliographic record
Abstract
Abstract Self-sovereign identity (SSI), an identity management system where individuals own and manage their digital identity, can improve access and management of one’s personal data. SSI is becoming feasible for the general public to use for their health and other personal data. Like any data system, when persons living with dementia no longer have capacity to provide informed consent, guardianship over their data is required. The purpose of this study was to examine the concept of guardianship within the context of SSI, specifically its application to persons living with dementia. This study followed a qualitative description approach. Seventeen semi-structured virtual interviews were conducted with persons living with dementia and care partners to elicit their perspectives on existing guardianship practices and guardianship within the context of SSI. Interviews were digitally recorded and transcribed verbatim. Conventional content analysis guided the analytic process. Participants had mixed impressions of existing guardianship practices. While some were positive, others thought existing practices failed to consider the complexity of caring for someone with dementia (e.g., presence of multiple guardians). Participants suggested that SSI has the potential to improve the security and safety of persons living with dementia who have had guardianship enacted (e.g., reduced risk of financial abuse.) Recommendations included ensuring that SSI guardianship processes are simple and flexible, building a user-friendly system that also considers the heterogeneity of persons living with dementia and their care partners. Overall, guardianship within the context of SSI was well received. Findings will be used to further inform the SSI guardianship processes.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".