Improving virtual cancer care for older Black adults: A qualitative study
Bibliographic record
Abstract
Abstract Background Health systems are rapidly promoting virtual cancer care models to improve cancer care of their populations. However, virtual cancer care can exacerbate inequities in cancer care for socially disadvantaged communities. Older Black cancer patients may face unique challenges to accessing and using virtual cancer care. This study focused on understanding the virtual cancer care experience of older Black patients, their caregivers and healthcare providers to identify strategies that can better support patient-centered care. Methods A theory-informed thematic analysis was conducted using data collected from six focus groups (N = 55 participants) conducted across ten Canadian provinces. Data was coded using the Patient Centered Care model and the synergies of oppression framework guided interpretation. Results Five overarching themes describe the experience of older Black patients, caregivers and healthcare providers in accessing and using virtual cancer care: Patient at the intersection of multiple systems of oppression; Shifting role of caregivers; Giving choice and choosing based on the purpose of care; Opportunity to meet health care needs through digital access; Communicating effectively through virtual care. We identified eight barriers to optimal virtual cancer care such as limited digital literacy, linguistic barriers in traditional African/Caribbean languages, and culturally mediated views of patients; and six facilitators to optimal virtual cancer care such as community-based cancer support groups, caregivers support and key features of digital technologies. Conclusions A multipronged approach that focuses on addressing barriers and leveraging culturally sensitive guides to virtual care can form the basis of health system efforts to improve access to virtual cancer care. A redesign of virtual cancer care programs, tailored to the needs of marginalized social groups like older Black patients can enhance the virtual 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 virtual cancer 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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".