Virtual Cancer Care Beyond the COVID-19 Pandemic: Patient and Staff Perspectives and Recommendations
Bibliographic record
Abstract
PURPOSE COVID-19 catalyzed rapid implementation of virtual cancer care (VC); however, work is needed to inform long-term adoption. We evaluated patient and staff experiences with VC at a large urban, tertiary cancer center to inform recommendations for postpandemic sustainment. METHODS All physicians who had provided VC during the pandemic and all patients who had a valid e-mail address on file and at least one visit to the Princess Margaret Cancer Centre in Toronto, Canada, in the preceding year were invited to complete a survey. Interviews and focus groups with patients and staff across the cancer center were analyzed using qualitative descriptive analysis and triangulated with survey findings. RESULTS Response rates for patients and physicians were 15% (2,343 of 15,169) and 41% (100 of 246), respectively. A greater proportion of patients than physicians were satisfied with VC (80.1 v 53.4%; P < .01). In addition, fewer patients than physicians felt that virtual visits were worse than those conducted in person (28.0 v 43.4%; P < .01) and that telephone and video visits negatively affected the human interaction that they valued (59.8% v 82.0%; P < .01). Major barriers to VC for patients were respect for care preferences and personal boundaries, accessibility, and equitable access. For staff, major barriers included a lack of role clarity, dedicated resources (space and technology), integration of nursing and allied health, support (administrative, clinical, and technical), and guidance on appropriateness of use. CONCLUSION Patient and staff perceptions and barriers to virtual care are different. Moving forward, we need to pay attention to both staff and patient experiences with virtual care since this will have major implications for long-term adoption into clinical practice.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".