User-Centered Design and Usability of a Culturally Adapted Virtual Survivorship Care App for Chinese Canadian Prostate Cancer Survivors: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Cultural adaptations of digital health innovations are a growing field. However, digital health innovations can increase health inequities. While completing exploratory work for the cultural adaptation of the Ned Clinic virtual survivorship app, we identified structural considerations that provided a space to design digitally connected and collective care. OBJECTIVE: This study used a community-based participatory research and user-centered design process to develop a cultural adaptation of the Ned Clinic app while designing to intervene in structural inequities. METHODS: The design process included primary data collection and qualitative analysis to explore and distill design principles, an iterative design phase with a multidisciplinary team, and a final evaluation phase with participants throughout the design process as a form of member checking and validation. RESULTS: Participants indicated that they found the final adapted prototype to be acceptable, appropriate, and feasible for their use. The changes made to adapt the prototype were not specifically culturally Chinese. Instead, we identified ways to strengthen connections between the survivor and their providers; improve accessibility to resources; and honor participants' desires for relationality, accountability, and care. CONCLUSIONS: We grounded the use of user-centered design to develop a prototype design that supports the acts of caring through digital technology by identifying and designing to resist structures that create health inequities in the lives of this community of survivors. By designing for collective justice, we can provide accessible, feasible, and relational care with digital health through the application of Indigenous and Black feminist ways of being and knowing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 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".