Co‐design of a digital app “WhatMatters” to support person‐centred care: A critical reflection
Bibliographic record
Abstract
BACKGROUND: People with dementia often do not receive optimal person-centred care (PCC) in care settings. Family members can play a vital role as care partners to support the person with dementia with their psychosocial needs. Participatory research that includes the perspectives of those with lived experience is essential for developing high-quality dementia care and practices. OBJECTIVE: Throughout 2021-2022, a mobile app, called WhatMatters, was co-developed to provide easy-to-access and personalised support for people with dementia in hospitals and long-term care homes, with input from patients/residents, family partners and healthcare staff. This article discusses and critically reflects on the experiences of patients/residents, family partners, and healthcare staff involved in the co-design process. METHODS: For the app development, we applied a participatory co-design approach, guided by a User Experience (UX) model. The process involved co-design workshops and user testing sessions with users (patients/residents, family partners, healthcare staff) to co-develop the WhatMatters prototype. We also conducted focus groups and one on one interviews with staff and caregiver participants to explore their experiences. Our research team, which also included patient partners, took part in regular team meetings during the app's development, where we discussed and reflected on the co-design process. Reflexive thematic analysis was performed to identify themes that represent the challenges and rewarding experiences of the users involved in the co-design process, which guided our overall reflective process. FINDINGS: Our reflective analysis identified five themes (1) clarifying the co-design process, (2) ensuring inclusive collaborations of various users, and (3) supporting expression of emotion in a virtual environment, (4) feeling a sense of achievement and (5) feeling valued. IMPLICATIONS: WhatMatters offers potential for providing personally relevant and engaging resources in dementia care. Including the voices of relevant users is crucial to ensure meaningful benefits for patients/residents. We offer insights and lessons learned about the co-design process, and explore the challenges of involving people with lived experiences of dementia in co-design work, particularly during the pandemic.
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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 | 0.044 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".