Delivery of Virtual Care in Oncology: Province-Wide Interprofessional Consensus Statements Using a Modified Delphi Process
Bibliographic record
Abstract
Virtual cancer care (i.e., teleoncology) was rapidly adopted during the COVID-19 pandemic to meet the needs of patients with cancer. However, there is a paucity of guidance for clinicians regarding virtual cancer care. We sought to develop consensus-based statements to guide the optimal provision of virtual care for clinicians caring for patients with cancer, using a modified Delphi consensus process with a 29-member panel consisting of an interprofessional group of clinicians caring for patients with cancer and patient representatives. The consensus process consisted of two rounds and one synchronous final consensus meeting. At the end of the modified Delphi process, 62 of 62 statements achieved consensus. Fifty-seven statements reached consensus in the first round of the process. Concerns regarding the ability to convey difficult news virtually and maintaining similar standards as in-person care without disproportionate strain on clinicians and patients were addressed in the consensus process. We achieved interprofessional consensus on virtual cancer care practices. Further research examining the impact of virtual cancer care on person-centred and clinical outcomes are needed to inform practices during the COVID-19 pandemic and beyond.
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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 | 0.260 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".