Health equity related challenges and experiences during the rapid implementation of virtual care during COVID-19: a multiple case study
Bibliographic record
Abstract
Abstract Background Virtual care quickly became of crucial importance to health systems around the world during the COVID-19 pandemic. Despite the potential of virtual care to enhance access for some communities, the scale and pace at which services were virtualized did not leave many organizations with sufficient time and resources to ensure optimal and equitable delivery of care for everyone. The objective of this paper is to outline the experiences of health care organizations rapidly implementing virtual care during the first wave of the COVID-19 pandemic and examine whether and how health equity was considered. Methods We used an exploratory, multiple case study approach involving four health and social service organizations providing virtual care services to structurally marginalized communities in the province of Ontario, Canada. We conducted semi-structured qualitative interviews with providers, managers, and patients to understand the challenges experienced by organizations and the strategies put in place to support health equity during the rapid virtualization of care. Thirty-eight interviews were thematically analyzed using rapid analytic techniques. Results Organizations experienced challenges related to infrastructure availability, digital health literacy, culturally appropriate approaches, capacity for health equity, and virtual care suitability. Strategies to support health equity included the provision of blended models of care, creation of volunteer and staff support teams, participation in community engagement and outreach, and securement of infrastructure for clients. We put our findings into the context of an existing framework conceptualizing access to health care and expand on what this means for equitable access to virtual care for structurally marginalized communities. Conclusion This paper highlights the need to pay greater attention to the role of health equity in virtual care delivery and situate that conversation around existing inequitable structures in the health care system that are perpetuated when delivering care virtually. An equitable and sustainable approach to virtual care delivery will require applying an intersectionality lens on the strategies and solutions needed to address existing inequities in the system.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".