Improving Digital Cancer Care for Older Black Adults: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Health systems are rapidly promoting digital cancer care models to improve cancer care of their populations. However, there is growing evidence that digital cancer care can exacerbate inequities in cancer care for communities experiencing social disadvantage, such as Black communities. Despite the increasing recognition that older Black adults face significant challenges in accessing and using health care services due to multiple socioeconomic and systemic factors, there is still limited evidence regarding how older Black adults' access and use digital cancer care. OBJECTIVE: This study aims to better understand the digital cancer care experience of older Black adults, their caregivers, and health care providers to identify strategies that can better support patient-centered digital cancer care. METHODS: A total of 6 focus group interviews were conducted with older Black adults living with cancer, caregivers, and health care providers (N=55 participants) across 10 Canadian provinces. Focus group interviews were recorded and transcribed. Through a theory-informed thematic analysis approach, experienced qualitative researchers used the Patient Centered Care model and the synergies of oppression conceptual lens to inductively and deductively code interview transcripts in order to develop key themes that captured the digital cancer care experiences of older Black adults. RESULTS: In total, 5 overarching themes describe the experience of older Black adults, caregivers, and health care providers in accessing and using digital cancer care: (1) barriers to access and participation in digital care services, (2) shifting caregivers' dynamics, (3) autonomy of choice and choosing based on the purpose of care, (4) digital accessibility, and (5) effective digital communication. We identify 8 barriers and 6 facilitators to optimal digital cancer for older Black adults. Barriers include limited digital literacy, linguistic barriers in traditional African or Caribbean languages, and patient concerns of shifting power dynamics when supported by their children for digital cancer care; and facilitators include community-based cancer support groups, caregiver support, and key features of digital technologies. CONCLUSIONS: These findings revealed a multifaceted range of barriers and facilitators to digital cancer care for older Black adults. This means that a multipronged approach that simultaneously focuses on addressing barriers and leveraging community strengths can improve access and usage of digital cancer care. A redesign of digital cancer care programs, tailored to the needs of most structurally marginalized groups like older Black adults, can enhance the digital care experience for all population groups. Public policies and organizational practices that address issues like availability of internet in remote areas, resources to support linguistic barriers, or culturally sensitive training are important in responding to the complexity of access to digital l cancer care. These findings have implications for other structurally marginalized and underresourced communities that have suboptimal access and usage of digital care.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".