Co-creating technological experiences to enhance dementia care partnerships
Bibliographic record
Abstract
Dementia is on the rise and society faces the challenge of how to manage its impacts. Challenging the biomedical discourse, sociocritical work has underscored relational aspects of caring and promoted ‘partnerships’ between persons with dementia (PwD), informal care partners, and formal care providers (the ‘partners’). Technological research has only begun to explore how innovation may enrich lived experiences with dementia beyond compensating for cognitive deficits or alleviating care ‘burdens’. My central thesis aims were to better understand the nature of care partnerships from the perspectives of PwD and family care partners, and to describe how co-creating technological experiences may impact care partnerships. Data were gathered from three qualitative studies. Study I co-designed with family care partners how they support PwD in activities, and how they envision technology complementing their care. Study II employed focus groups with adult children that constructed an understanding of how adult children sustain caring within their family and formal care contexts. Study III used a multiple case study to describe how four care networks adapted to new technologies, and how doing so impacted care practices. Toward my central thesis aims, these findings together demonstrate that care partnerships are comprised of diverse and interdependent care relationships. Partners exercise different forms of knowledge, expertise, and perspectives in ways that may converge, complement, or conflict with one another. Partners interact by responding and adapting to care changes, balancing and negotiating with one another, entrusting and diffusing care responsibilities, and learning and growing throughout their care journeys. Whether co-creating technological experiences challenges or enhances care partnerships is influenced by how partners make meaning with technology, learn and foster technological support resources, adapt care practices, and reconfigure their care relationships through technology use. Future work is encouraged to adopt relational approaches to understanding and designing to enrich lives with dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".