Harnessing New and Existing Virtual Platforms to Meet the Demand for Increased Inpatient Palliative Care Services During the COVID-19 Pandemic: A 5 Key Themes Literature Review of the Characteristics and Barriers of These Evolving Technologies
Bibliographic record
Abstract
The COVID-19 pandemic has increased the demand for end-of-life services and bereavement support, and in many areas of the world, in-person palliative care is struggling to meet these needs. Local infection control measures result in limited visitation rights in hospital and patients are often dying alone. For many years, virtual platforms have been used as a validated alternative to in-person consults for outpatient and home-based palliative care; however, the feasibility and acceptability of a virtual inpatient equivalent is less studied. Virtual inpatient palliative care may offer a unique opportunity for patients to have meaningful interactions with their care team and family while isolated in hospital or in hospice. This narrative review examines strategies employed during the COVID-19 pandemic to implement virtual palliative care services in the inpatient setting. Five key themes were identified in the literature between January 2020-March 2021 in the LitCovid NCBI database: 1) overall acceptability of virtual inpatient palliative care during the pandemic, 2) important logistical considerations when developing a virtual inpatient palliative care platform, 3) commonly used technologies for delivering virtual services, 4) strategies for harnessing human resources to meet increased patient volume, and 5) challenges of virtual inpatient palliative care implementation. Upon review, telepalliative care can meet the increased demand for safe and accessible inpatient palliative care during a pandemic; however, in some circumstances in-person services should still be considered. The decision for which patients receive what format of care-in-person or virtual-should be decided on a case-by-case basis.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".