Current and Future Directions Using Virtual Avenues for Care Delivery Across the Cancer Continuum
Bibliographic record
Abstract
Oncology nurses have long been at the forefront of virtual care, transitioning from telenursing to technology-driven delivery methods that address the evolving needs of cancer patients. Initially developed to overcome barriers to care for rural and underserved populations, virtual care has grown into a critical component of oncology practice. Oncology nurses play a central role in providing timely, personalized, and holistic care, leveraging tools such as remote monitoring, patient-reported outcomes, and mHealth platforms. However, the rapid adoption of virtual care demands a broader focus to sustain its impact. This commentary explores the need to clearly define the role of oncology nurses in virtual care, emphasizing leadership in digital health, the integration of hybrid care models, and workforce training. By addressing these priorities, virtual care can continue to enhance patient outcomes, strengthen nursing-led interventions, and expand the scope of oncology nursing, positioning it as an essential and enduring facet of cancer care delivery.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".