Virtual Oncology Appointments during the Initial Wave of the COVID-19 Pandemic: An International Survey of Patient Perspectives
Bibliographic record
Abstract
There has been rapid implementation of virtual oncology appointments in response to the COVID-19 pandemic, particularly in its first wave. Our objective was to assess patterns and perspectives towards virtual oncology appointments during the pandemic among patients with cancer undergoing active treatment. We conducted an international Internet-based cross-sectional survey. Participants were eligible if they (1) were ≥18 years of age; (2) had been diagnosed with cancer (3) were currently undergoing cancer treatment, and (4) spoke English or French. Between 23 April 2020 and 9 June 2020, 381 individuals accessed the survey, with 212 actively undergoing treatment for cancer, including 27% with colorectal, 21% with breast, 7% with prostate and 7% with lung cancer. A total of 52% of respondents were from Canada and 35% were from the United States. Many participants (129, 62%) indicated having had a virtual oncology appointment during the COVID-19 pandemic and most were satisfied with their experience (83%). We found older participants (≥50 years; adjusted OR 0.22, 95% CI 0.06 to 0.85 compared to <50 years) and those with shortest duration of treatment (≤3 months; adjusted OR 0.06; 95% CI 0 to 0.69 compared to >12 months) were less likely to be satisfied with virtual oncology appointments. Virtual health platforms used differed across countries with higher telephone use in Canada (87%) and other countries (86%) as compared to the United States (54%; p-value < 0.05), where there was higher use of video conferencing. Altogether, our findings demonstrate favorable patient perspectives towards virtual oncology appointments experienced during the first wave of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".