Impact of the coronavirus disease 2019 pandemic on delivery of and models for supportive and palliative care for oncology patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Supportive and palliative care services have been an important component of the overall COVID-19 pandemic response. However, significant changes in the provision and models of care were needed in order to optimize the care delivered to vulnerable cancer patients. This review discusses the evolution of palliative and supportive care service in response to the pandemic, and highlights remaining challenges. RECENT FINDINGS: Direct competition for resources, as well as widespread implementation of safety measures resulted in major shifts in the mode of assessment and communication with cancer patients by supportive care teams. Telemedicine/virtual consultation and follow-up visits became an integral strategy, with high uptake and satisfaction amongst patients, families and providers. However, inequities in access to the required technologies were sometimes exposed. Hospice/palliative care unit (PCU) bed occupancy declined markedly because of restrictive visitation policies. Collection of patient-reported outcome (PRO) data was suspended in many cancer centers, with resulting under-recognition of anxiety and depression in ambulatory patients. As in many other areas, disparities in delivery of supportive and palliative care were magnified by the pandemic. SUMMARY: Virtual care platforms have been widely adopted and will continue to be used to include a wider circle of family/friends and care providers in the provision of palliative and supportive care. To facilitate equitable delivery of supportive care within a pandemic, further research and resources are needed to train and support generalists and palliative care providers. Strategies to successfully collect PROs from all patients in a virtual manner must be developed and implemented.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".