Delivering Virtual Cancer Rehabilitation Programming During the First 90 Days of the COVID-19 Pandemic: A Multimethod Study
Bibliographic record
Abstract
OBJECTIVE: To describe the adaptations made to implement virtual cancer rehabilitation at the onset of the coronavirus disease 2019 pandemic, as well as understand the experiences of patients and providers adapting to virtual care. DESIGN: Multimethod study. SETTING: Cancer center. PARTICIPANTS: A total of 1968 virtual patient visits were completed during the study period. Adult survivors of cancer (n=12) and oncology health care providers (n=12) participated in semi-structured interviews. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Framework-driven categorization of program modifications, qualitative interviews with patients and providers, and a comparison of process outcomes with the previous 90 days of in-person care via referrals, completed visits and attendance, method of delivery, weekly capacities, and wait times. RESULTS: The majority of program visits could be adapted to virtual delivery, with format, setting, and content modifications. Virtual care demonstrated an increase or maintenance in the number of completed visits by appointment type compared with in-person care, with attendance ranging from 80%-93%. For most appointment types, capacities increased, whereas wait times decreased slightly. Overall, 168 patients (11% of all assessments and follow-ups) assessed virtually were identified by providers as requiring an in-person appointment because of reassessment of musculoskeletal and/or neurologic impairment (n=109, 65%) and lymphedema (n=59, 35%). The interviews (n=24) revealed that virtual care was an acceptable alternative in some circumstances, with the ability to (1) increase access to care; (2) provide a sense of reassurance during a time of isolation; and (3) provide confidence in learning skills to self-manage impairments. CONCLUSIONS: Many appointments can be successfully adapted to virtual formats to deliver cancer rehabilitation programming. Based on our findings, we provide practical recommendations that can be implemented by providers and programs to facilitate the adoption and delivery of virtual care.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".