Multiple stakeholders’ perspectives on improving virtual cancer care for older Black adults
Bibliographic record
Abstract
Introduction: Cancer is the leading cause of death in Canada. Social distancing regulations during the COVID-19 pandemic drove the rapid transitions of health care systems from in-person to virtual cancer care. Virtual care refers to the secure usage of various forms of information and communication technologies such as phone calls, video conferencing, or patient portals to facilitate/optimize the quality of patient care. Virtual care has been shown to be effective for a variety of uses in cancer care, such as pre-treatment discussions and counselling purposes. However, recent research suggests that although virtual cancer care (VCC) can enhance access to health services for some communities, it can also exacerbate health inequities for socially disadvantaged groups such as Racialized communities that may not be able to afford necessary digital technology required for virtual care. Furthermore, older adults face additional challenges when interacting with technology including restricted ability to use technology due to physical or cognitive limitations. Although Black people in Canada are reported to have the lowest rates of access to digital health technologies, it is still unclear how race affects VCC in Canada. It is essential that VCC is delivered in a manner that meets the needs of all communities and does not exacerbate inequities between the socially advantaged and socially disadvantaged communities. The objective of this study was to better understand the VCC experience of older Black patients, their caregivers and healthcare providers. Methods: Qualitative studies are appropriate to capture the lived experiences of individuals/communities of a social phenomena such as VCC. We used a theory-informed thematic analysis, using data collected from six focus groups (N= 55 participants: 40 patients and caregivers, and 15 healthcare providers) conducted across ten Canadian provinces. Qualitative data analysis was facilitated by the Nvivo 11 software. Data was coded using the Patient Centred Care model and the Synergies of Oppression framework guided interpretation. Results: The experiences of older Black patients, their caregivers and healthcare providers in using VCC were captured in five overarching themes: 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. Eight barriers to optimal VCC such as limited digital literacy, linguistic barriers in traditional African/Caribbean languages, and culturally mediated views of patients were identified. We also identified six facilitators to optimal VCC such as community-based cancer support groups, caregivers support and key features of digital technologies. Conclusion: This study suggests that efforts to improve the quality of VCC necessitates a multipronged approach that addresses barriers while leveraging culturally sensitive guides to VCC. Initiative to redesign VCC programs tailored to the needs of socially disadvantaged communities like older Black patients may improve the virtual care experience for the whole population. Public policies and 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 VCC
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 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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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".