Oncologists’ Satisfaction with Virtual Care: A Questionnaire
Bibliographic record
Abstract
Introduction: Although virtual care (VC) has become an integral part of oncology care and healthcare delivery, clinicians’ perspectives on and satisfaction with this modality are not well understood. Methods: Using a National Network Forum framework and expert panel review, we developed a questionnaire to measure oncologists’ satisfaction with VC. The questionnaire was distributed to Canadian oncologists through medical society email lists (n = 1541). We used a 5-point Likert scale to capture their responses, which included strongly disagree (1), disagree (2), undecided (3), agree (4), and strongly agree (5). Results: A total of 61 oncologists and/or oncology trainees, of 768 (7.9%) who opened their email, completed questionnaires between October 2022 and January 2023. Every questionnaire item had a response rate greater than 98%. Seventy-two percent of the respondents were satisfied with VC. Oncologists who were less comfortable with technology were more likely to report lower levels of satisfaction (p < 0.001, Wilcoxon rank-sum). The questionnaire items that received the highest levels of agreement were related to VC reducing costs and improving access for patients and concerns about missing a diagnosis and assessing patients’ functional status. The questionnaire items that received the greatest disagreement were related to VC improving access for patients with language barriers, VC being associated with time-savings for clinicians, improvements in clinical efficacy, and more readily available lab tests. Conclusions: Most of the oncologists surveyed are satisfied with VC; however, there are some concerns with VC that need to be addressed. Future research on optimizing VC should address clinicians’ concerns, in addition to addressing the patient experience.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".