A Delphi Study on Dementia Care Technology Use and How to Mitigate Risks: Design to Implementation
Bibliographic record
Abstract
Abstract What information is needed to navigate person-centered technology use in dementia care? The threefold aim of this Delphi study was to learn which technologies will be most prevalent in dementia care in 5 years, understand benefits and risks, and identify specific options to mitigate risks. Twenty-one interdisciplinary domain experts from academia and industry in aging and technology in the U.S. and Canada participated, an 84% response rate. Technologies rated most likely to cause value tensions were also predicted to be most prevalent and will be described, along with the identified risks. Suggestions to mitigate the risks are categorized as follows: intervene during design; make specific technical choices; build in choice and control; require data transparency; place restrictions on data use and ensure security; enable informed consent; and proactively educate users. The specific recommendations that are relevant to designers, clinicians, researchers, ethicists, and policy makers will be presented and discussed.
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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 | 0.160 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".