Honoring the Care Experiences of Chinese Canadian Survivors of Prostate Cancer to Cultivate Cultural Safety and Relationality in Digital Health: Exploratory-Descriptive Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Prostate cancer (PCa) is the most commonly diagnosed nonskin cancer for Canadian men and has one of the highest 5-year survival rates, straining systems to provide care. Virtual care can be one way to relieve this strain, but survivors’ care needs and technology use are influenced by intersecting social and cultural structures. Cultural adaptation has been posited as an effective method to tailor existing interventions to better serve racialized communities, including Chinese men. However, cultural adaptations may inadvertently draw attention away from addressing structural inequities. OBJECTIVE This study used qualitative methods to (1) explore the perceptions and experiences of Chinese Canadian PCa survivors with follow-up and virtual care, and (2) identify implications for the cultural adaptation of a PCa follow-up care app, the Ned (no evidence of disease) Clinic. METHODS An axiology of relational accountability and a relational paradigm underpinned our phenomenologically informed exploratory-descriptive qualitative study design. A community-based participatory approach was used, informed by cultural safety and user-centered design principles, to invite Chinese Canadian PCa survivors and their caregivers to share their stories. Data were inductively analyzed to explore their unmet needs, common experiences, and levels of digital literacy. RESULTS Unmet needs and technology preferences were similar to broader trends within the wider community of PCa survivors. However, participants indicated that they felt uncomfortable, unable to, or ignored when expressing their needs. Responses spoke to a sense of isolation and reflected a reliance on culturally informed coping mechanisms, such as “eating bitterness,” and familial assistance to overcome systemic barriers and gaps in care. Moreover, virtual care was viewed as “better than nothing;” it did not change a perceived lack of focus on improving quality of life or care continuity in survivorship care. Systemic changes were identified as likely to be more effective in improving care delivery and well-being rather than the cultural adaptation of Ned for Chinese Canadians. Participants’ desires for care reflected accessibility issues that were not culturally specific to Chinese Canadians. CONCLUSIONS Chinese Canadian survivors are seeking to strengthen their connections in a health care system that provides privacy and accessibility, protects relationality, and promotes transparency, accountability, and responsibility. Designing “trickle-up” adaptations that address structural inequities and emphasize accessibility, relationality, and privacy may be more effective and efficient at improving care than creating cultural adaptations of interventions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".