A qualitative study on the Virtual Emergency Department care experiences of equity-deserving populations
Bibliographic record
Abstract
Patients from equity-deserving populations, such as those who are from racialized communities, the 2SLGBTQI+ community, who are refugees or immigrants, and/or who have a disability, may experience a unique set of challenges accessing virtual models of care. The objective of this qualitative study was to describe the experiences of patients from equity-deserving communities and their family members who received care from a Virtual Emergency Department (ED) in Toronto, Canada. Forty-three participants (36 patients and 7 family caregivers) with different and intersecting identities who used the Virtual ED participated in the study. Semi-structured interviews were conducted to explore reasons for accessing the Virtual ED, barriers to access, and how the Virtual ED met their care needs and expectations, including ways their experience could have been improved. Thematic analysis was used to identify themes from the data. Patients from equity-deserving populations described negative past experiences with ED in-person care, which included recounts of discrimination or culturally insensitive care while waiting to see the ED physician or nurse. Conversely, participants found the Virtual ED to be a socially and culturally safe space since they could now by-pass the waiting room experience. However, virtual care could not replace in-person care for certain issues (e.g., physical exam), and there was a need for greater promotion of the service to specific communities that might benefit from having access to the Virtual ED. Targeted outreach to help raise awareness of the service to equity-deserving communities is an important future direction.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".