Exploring How Persons with Dementia and Care Partners Collaboratively Appropriate Information and Communication Technologies
Bibliographic record
Abstract
Persons with dementia and their care partners have been found to adapt their own technological arrangements using commercially available information and communication technologies (ICTs). Yet, little is known about these processes of technology appropriation and how care practices are impacted. Adopting a relational perspective of care, we longitudinally examined how four family care networks appropriated a new commercial ICT service into their existing technological arrangements and care practices. Cross-case analysis interpreted collaborative appropriation to encompass two interrelated processes of creating and adapting technological practices and negotiating and augmenting care relationships . Four driving forces were also proposed: motivating meanings that actors ascribe to the technology and its use; the learnability of the technology and actors’ resourcefulness ; the establishment of responsive and cooperative care practices ; and the qualities of empathy and shared power in care relationships . The importance of technological literacy, learning, meaning-making, and the nature and quality of care relationships are discussed. Future work is urged to employ longitudinal and naturalistic approaches, and focus design efforts on promoting synergistic care relationships and care practices.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".