Perspectives on Virtual Care for Childhood Cancer Survivors in Non-Metropolitan Areas during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic paved the way for the widespread use of virtual care for childhood cancer survivors (CCSs). CCSs were virtual recipients of diverse care, including long-term follow-up (LTFU), primary care, mental health care, and several others. Virtual care comes with well-documented benefits and challenges. These are further magnified for CCSs living in rural or non-metropolitan areas. Here, we describe the virtual care of CCSs from two Upper Midwest cities with well-established childhood cancer survivor programs within large comprehensive cancer centers in the United States. CCSs from non-metropolitan areas, especially CCSs with two or more late effects, used virtual care more often during the COVID-19 pandemic compared to CCSs from metropolitan areas. A review of the related literature is also included and the identified challenges in providing virtual care, such as privacy concerns, technology-connectivity constraints, and medical license restrictions. Despite these limitations, the care of CCSs has evolved to leverage virtual care and its ability to increase access for patients and promote continuity of care for CCSs living in rural areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".