MétaCan
Menu
Back to cohort
Record W4389103209 · doi:10.2196/49349

Honoring the Care Experiences of Chinese Canadian Survivors of Prostate Cancer to Cultivate Cultural Safety and Relationality in Digital Health: Exploratory-Descriptive Qualitative Study

2023· article· en· W4389103209 on OpenAlexaffabout
Karen Young, Ting Xiong, Rachel Lee, Ananya Banerjee, Myles Leslie, Wellam F. Yu Ko, Julia Yu Jia Guo, Quỳnh Phạm

Bibliographic record

VenueJournal of Medical Internet Research · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of OttawaUniversity of WaterlooBritish Columbia Centre of Excellence for Women's HealthMcGill UniversityUniversity of CalgaryUniversity of British ColumbiaPublic Health OntarioToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsQualitative researchExploratory researchParticipatory action researchPsychological interventionHealth carePsychologyMedicineNursingSociology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0210.009
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.117
GPT teacher head0.477
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicCancer survivorship and careFrench-language works237,207