Designing a Good Place: Culturally Safe Virtual Care for Chinese Canadian Prostate Cancer Survivors through Community-based Participatory Methods
Bibliographic record
Abstract
Cultural adaptations of digital health interventions have been posited as a pathway to relieve resource constraints in an era of thinning healthcare services, while increasing access to care for communities made vulnerable. However, in settler colonial nation-states such as Canada, this approach may problematize “minority” culture and identity as the cause of structural health inequities. This research programme sought to qualitatively (1) explore the perceptions and experiences of Chinese Canadian prostate cancer survivors with follow-up and virtual care to identify structures that influenced their care (Study 1), and (2) develop and evaluate a community-based, culturally safe adaptation of the patient-facing Ned Clinic application (Study 2). Rather than adapting interventions using cultural sensitivity or competency, we demonstrate that by shifting paradigms, resurfacing the desires of our relations, and designing for relational accountability and cultural safety, we may be able to regenerate places of caring through digital health for more communities instead.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".