Codesign of a framework to support compassionate care appropriate to technology use by people with cognitive decline
Bibliographic record
Abstract
PURPOSE: To further develop and begin to validate the Framework for Compassionate Use of Technology with Persons with Cognitive Decline and their Care Partners. METHODS: We used an experience-based co-design approach. Phase 1 involved interviews with participants with cognitive decline and their care partners to increase our understanding of their perceptions of home monitoring technology use. Phase 2 involved Focus group meetings with participants with cognitive decline, their care partners, healthcare providers and technology researchers to seek feedback on the framework and develop tools to support a compassionate approach to the use of home monitoring technology. RESULTS: Domains and categories identified during interviews were (a) Ethical Principles including Addressing Privacy Concerns, Considering Dignity, Balancing Risks with Benefits, and Supporting Autonomy; and (b) Usefulness and Preferences including Technology Utility, Need for Maintenance, and Preferences for Information Receipt. These findings along with focus group discussions aligned with and further elaborated the framework. Focus groups also informed the development of tools to support a compassionate approach to recommending and selecting home monitoring technology. CONCLUSIONS: The study provides initial support for the framework. More research is needed to test applicability and the usefulness in practice settings.
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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 | 0.053 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".