Patient and Provider Experiences With Virtual Care in a Large, Ambulatory Care Hospital in Ontario, Canada During the COVID-19 Pandemic: Observational Study
Bibliographic record
Abstract
BACKGROUND: Virtual care use increased during the COVID-19 pandemic. The impact of that shift on patient and provider experiences is unclear. OBJECTIVE: We evaluated patient and provider experiences with virtual visits across an academic, ambulatory hospital in Toronto, Canada and assessed predictors of positive experience with virtual care. METHODS: Survey data were analyzed from consenting patients who attended at least one virtual visit (video or telephone) and from consenting providers who delivered at least one virtual visit. Distributions for demographic variables and responses to survey questions are reported, with statistical significance assessed using chi-square tests and t tests. Ordinal logistic regression analysis was used to identify any patient predictors of responses. RESULTS: During the study period, 253 patients (mean age 45.1, SD 15.6 years) completed 517 video visit surveys, and 147 patients (mean age 41.6, SD 16.4 years) completed 209 telephone visit surveys. A total of 75 and 94 providers completed the survey in June 2020 and June 2021, respectively. On a scale from 1 to 10 regarding likelihood to recommend virtual care to others, fewer providers rated a score of 8 or above compared with patients (providers: 62/94, 66% for video and 49/94, 52% for telephone; patients: 415/517, 80% for video and 150/209, 72% for telephone). Patients of non-White ethnicity had lower odds of rating a high score of 9 or 10 compared with White patients (odds ratio 0.52, 95% CI 0.28-0.99). CONCLUSIONS: Patient experiences with virtual care were generally positive, but provider experiences were less so. Findings suggest potential differences in patient experience by ethnicity, warranting further investigation into equity concerns with virtual 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".