“It saved me from the emergency department”: A qualitative study of patient experience of virtual urgent care in Ontario
Bibliographic record
Abstract
INTRODUCTION: In response to the COVID-19 pandemic, the Ontario Ministry of Health introduced a pilot program of 14 virtual urgent care (VUC) initiatives across the province to encourage physical distancing and provision of care by telephone and video-enabled visits. The implementation of the VUC pilot is currently being evaluated by an external academic team. The objective of this study was to understand patient experiences with VUC to determine barriers and facilitators to optimal virtual care as it rapidly expands during the current pandemic and beyond. METHOD: The qualitative component of the evaluation used one-on-one telephone interviews with patients, families, providers, and program administrators as the main method of data collection. Patient and family participants were invited to participate by the triage nurse after their VUC visit. Data analysis, using thematic analysis, occurred in conjunction with data collection to monitor emerging themes and areas for further exploration. RESULTS: Between April and October 2021, we completed 14 patient and/or family interviews from a representative cross-section of 6 pilot sites. Participants had a range of presenting complaints including infection, injury, medication side effects, and abdominal pain. The vast majority of participants were female (90%), and 70% were VUC patients themselves. Our analysis identified three key themes in the data which characterise patient and family member experience with VUC: a) emphasis on access to the ED; b) efficiency and quality of care; c) obtaining reassurance and next steps. CONCLUSION: Virtual care options are valued by patients and families; however, the nature of care needed by those accessing VUC and who can best provide that care needs to be evaluated to position it for sustainability. Understanding how virtual care performs from both a provider and patient perspective during the current crisis has implications for designing alternative care options beyond the COVID-19 pandemic.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".